Beginner on AI Knowledge Base

AI Capabilities and Limitations

Mon, 01 Jan 0001 00:00:00 +0000

A concise introduction to what AI can and cannot do — the companion course to the AI Fluency Framework. Explains the machine properties (how AI actually works) that the 4D Framework competencies respond to. Essential for product managers and business leaders who need to understand AI capabilities at a conceptual level before making product decisions. Pairs with the AI Landscape for Product Leaders learning path.

AI Fluency: Framework & Foundations

Mon, 01 Jan 0001 00:00:00 +0000

Anthropic’s foundational AI fluency course teaching the 4D Framework — Delegation, Description, Discernment, and Diligence — for effective AI collaboration. Co-created with university professors (UCC, Ringling College), this goes beyond prompt engineering to teach a systematic approach for working with AI effectively, efficiently, ethically, and safely. The 4D Framework provides a mental model that transfers across any AI tool, not just Claude. Start here before the audience-specific fluency courses.

AI for Everyone

Mon, 01 Jan 0001 00:00:00 +0000

Andrew Ng’s definitive non-technical introduction to AI — the single best starting point for anyone who wants to understand what AI can and cannot do without writing a line of code. Covers AI terminology, what machine learning is, what data it needs, and how to spot opportunities for AI in your organization. Over 4 million enrollments make this the most popular AI course ever created. Take this before any other course if you’re new to AI.

Prompt Engineering Across Providers

Mon, 01 Jan 0001 00:00:00 +0000

Master prompt engineering by studying best practices from Anthropic, OpenAI, and Mistral. Then see how DSPy challenges the entire paradigm by replacing prompts with programs.

Comparing approaches across providers gives you deeper intuition than studying any single provider’s guide.

Spec-Driven Development

Mon, 01 Jan 0001 00:00:00 +0000

Wikipedia overview of specification-driven development as a software methodology where formal specs precede and drive implementation.

The Economic Potential of Generative AI: The Next Productivity Frontier

Mon, 01 Jan 0001 00:00:00 +0000

Comprehensive analysis of generative AI’s economic impact across industries, quantifying $2.6-4.4 trillion in annual value potential and identifying which business functions will see the greatest transformation.

The EU's AI Act and How Companies Can Achieve Compliance

Mon, 01 Jan 0001 00:00:00 +0000

Practical guide to the EU AI Act — the world’s first comprehensive AI law — explaining risk categories, compliance requirements, and what companies need to do to prepare.

What Is an AI Agent?

Mon, 01 Jan 0001 00:00:00 +0000

McKinsey’s explainer on AI agents — what they are, how they differ from chatbots, and how they will impact business operations and the workforce.

What Product Managers Need to Know About LLMs

Mon, 01 Jan 0001 00:00:00 +0000

A comprehensive non-technical guide to LLM capabilities, limitations, and practical applications for product managers building AI-powered features.

16 Changes to the Way Enterprises Are Building and Buying Generative AI

Mon, 01 Jan 0001 00:00:00 +0000

Data-driven analysis of how enterprise leaders are increasing AI budgets and shifting toward multi-model, open-source strategies while moving from experimentation to production.

AI Fluency for Educators

Mon, 01 Jan 0001 00:00:00 +0000

Applies the 4D AI Fluency Framework specifically to teaching and institutional strategy. Designed for faculty, instructional designers, and educational leaders who need to integrate AI into curricula responsibly. Covers practical scenarios: classroom policies, assignment design, academic integrity, and helping students develop AI skills. Take this after the Framework & Foundations course for education-specific applications.

Designing a Responsible AI Program? Start with this Checklist

Mon, 01 Jan 0001 00:00:00 +0000

Eight critical questions organizations should answer before implementing enterprise-wide responsible AI programs to avoid rushing deployment and wasting resources.

Introduction to AI

Mon, 01 Jan 0001 00:00:00 +0000

Created by the University of Helsinki, this is the most accessible and comprehensive non-technical AI course available. With over 2 million signups from 170+ countries and a 4.8/5 rating, it covers AI concepts, machine learning, neural networks, and societal implications through interactive exercises. The 30-hour estimate is generous — most complete it in 15-20 hours. Unlike Andrew Ng’s course which is video-based, this is primarily text-based with embedded exercises, making it excellent for self-directed learners who prefer reading to watching.

Awesome Spec-Driven Development

Mon, 01 Jan 0001 00:00:00 +0000

Community-curated list of SDD tools, articles, frameworks, and resources. A living index of the SDD ecosystem that is updated as new tools emerge.

ChatGPT Prompt Engineering for Developers

Mon, 01 Jan 0001 00:00:00 +0000

The most popular prompt engineering course available — taught by Isa Fulford (OpenAI) and Andrew Ng. In just one hour, covers prompting best practices, iterative prompt development, summarizing, inferring, transforming, and expanding text. Despite the OpenAI branding, the techniques apply to any LLM. The concise format makes it the ideal first technical introduction to prompt engineering before exploring provider-specific approaches in Anthropic’s and OpenAI’s documentation.

Generative AI Glossary for Business Leaders

Mon, 01 Jan 0001 00:00:00 +0000

Plain-language glossary of essential generative AI terms designed for everyone in a company regardless of technical background.

Introduction to Generative AI

Mon, 01 Jan 0001 00:00:00 +0000

A 30-minute micro-course that explains what generative AI is, how it works, and how it differs from traditional machine learning. Google’s concise format makes this the fastest on-ramp to understanding LLMs — take this if you need a quick conceptual foundation before diving into provider-specific content. Covers transformer architecture at a conceptual level without code.

Vibe Coding Is Not AI-Assisted Engineering

Mon, 01 Jan 0001 00:00:00 +0000

Distinguishes vibe coding from specification-driven AI-assisted engineering, arguing that specifications are the differentiator between amateur and professional AI-augmented development.

AI Fluency for Nonprofits

Mon, 01 Jan 0001 00:00:00 +0000

Applies AI fluency concepts specifically to nonprofit contexts — fundraising, program delivery, volunteer management, and impact measurement. Addresses the unique constraints nonprofits face: limited budgets, data sensitivity, and mission alignment. Take this after the Framework & Foundations course for sector-specific applications.

Claude 101

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use Claude effectively for everyday work tasks. Covers Claude’s core features, capabilities, and resources for more advanced learning. This is the official product training course for Claude — take it to understand Claude’s strengths and how to get the most out of it before diving into API-level courses or the developer track.

Generative AI Explained

Mon, 01 Jan 0001 00:00:00 +0000

NVIDIA’s no-code introduction to generative AI, designed for anyone regardless of technical background. Covers what generative AI is, how large language models work at a conceptual level, and the applications transforming industries. Shorter and more focused than Elements of AI or AI for Everyone — take this for NVIDIA’s GPU-centric perspective on why generative AI requires different infrastructure than traditional software.

MCP Fundamentals

Mon, 01 Jan 0001 00:00:00 +0000

Go from zero to a solid understanding of the Model Context Protocol. This path covers the core architecture, primitives (tools, resources, prompts), transport mechanisms, and how clients and servers interact.

By the end you’ll understand the MCP mental model well enough to evaluate MCP servers, build simple integrations, and read the specification confidently.

SDD Overview

Mon, 01 Jan 0001 00:00:00 +0000

Practical overview of specification-driven development fundamentals, aimed at teams considering adoption.

Claude Code 101

Mon, 01 Jan 0001 00:00:00 +0000

Quick introduction to Claude Code — Anthropic’s agentic coding tool that lives in your terminal. Covers setup, basic usage, and key features. This is the video companion to the Claude Code Mastery learning path: the course gets you started quickly, the path provides comprehensive documentation coverage.

Generative AI Foundations

Mon, 01 Jan 0001 00:00:00 +0000

AWS’s free introduction to generative AI — covers foundation models, training approaches, and how generative AI fits into the AWS ecosystem. Provides the AWS perspective on AI infrastructure, which complements the Anthropic and Google viewpoints in this knowledge base. Useful for teams evaluating Bedrock as their AI deployment platform.

Introduction to Responsible AI

Mon, 01 Jan 0001 00:00:00 +0000

Google’s concise introduction to responsible AI practices — covers fairness, interpretability, privacy, and security in AI systems. At just 30 minutes, this is the fastest way to understand the principles that should guide any AI deployment. Pairs well with the AI Safety learning paths in this knowledge base for a more comprehensive view of safety across providers.

SDD with AI: Open Source Toolkit

Mon, 01 Jan 0001 00:00:00 +0000

GitHub’s introduction to SDD with AI, announcing the open-source Spec Kit toolkit and explaining how specifications fit into the broader development ecosystem.

Intro to Machine Learning

Mon, 01 Jan 0001 00:00:00 +0000

The fastest hands-on introduction to machine learning — 3 hours from zero to building your first model. Kaggle’s browser-based notebooks mean no setup required, and the 30 free GPU hours per week let you experiment immediately. Covers decision trees, model validation, underfitting/overfitting, and random forests with real datasets. Take this if you want to get your hands dirty with ML code in an afternoon, not a semester.

Spec-Driven Development Fundamentals

Mon, 01 Jan 0001 00:00:00 +0000

Learn the specification-driven development methodology from concept through practice. Understand why SDD emerged alongside AI coding tools, how to write effective specifications, and how to use tools like GitHub Spec Kit to formalize the workflow.

Prerequisites: Familiarity with Claude Code (complete Claude Code Mastery first) helps you apply SDD concepts immediately, but is not strictly required.

The Rise of Spec-Driven Development

Mon, 01 Jan 0001 00:00:00 +0000

Trend analysis of SDD’s emergence as the structured counterpart to vibe coding, explaining why specifications became essential as AI coding tools matured.

Azure AI Fundamentals (AI-900)

Mon, 01 Jan 0001 00:00:00 +0000

Microsoft’s structured learning path for the AI-900 certification — covers AI workloads, machine learning principles, computer vision, NLP, and generative AI on Azure. The most enterprise-oriented AI fundamentals course available, designed for professionals who need to understand AI in the context of cloud services and business applications. Each module has knowledge checks and the path directly prepares you for the AI-900 certification exam.

Getting Started with GitHub Spec Kit

Mon, 01 Jan 0001 00:00:00 +0000

Hands-on tutorial for GitHub Spec Kit CLI, covering installation, spec creation, validation, and integration with AI coding workflows.

TDD vs BDD vs SDD

Mon, 01 Jan 0001 00:00:00 +0000

Comparison of test-driven, behavior-driven, and specification-driven development, highlighting when each approach is most appropriate and how they complement each other.

Build with Claude — Overview

Mon, 01 Jan 0001 00:00:00 +0000

Central hub for understanding how to build applications with the Claude API.

Introduction to the Anthropic Platform

Mon, 01 Jan 0001 00:00:00 +0000

Overview of the Anthropic API platform, Claude models, and what you can build.

Tool Use — Overview

Mon, 01 Jan 0001 00:00:00 +0000

How Claude calls external tools and functions — the foundation for building agentic systems.

Get Started

Mon, 01 Jan 0001 00:00:00 +0000

API key setup, first API call, and quickstart for the Anthropic platform.

AI Landscape for Product Leaders

Mon, 01 Jan 0001 00:00:00 +0000

A non-technical introduction to the AI provider landscape for product managers, executives, and business leaders. This path builds your understanding of what AI models can do, how they’re priced, and how to think about provider selection — without requiring any coding knowledge.

By the end of this path, you’ll be able to: evaluate AI provider options, understand cost structures for budgeting, and frame build-vs-buy decisions for your organization. You’ll have the vocabulary and mental models to discuss AI capabilities with your engineering team and present AI strategy to leadership.

AI Use Cases & Business Applications

Mon, 01 Jan 0001 00:00:00 +0000

A practical guide to identifying, evaluating, and prioritizing AI use cases for your product or organization. See concrete examples of AI in production — from customer support to document processing — and learn frameworks for assessing ROI and choosing the right approach.

By the end of this path, you’ll be able to: identify high-value AI use cases in your organization, evaluate whether a use case needs simple AI or complex agents, build an ROI-backed business case for AI investment, and have informed conversations with engineering about implementation feasibility.

Understanding AI Agents & Protocols

Mon, 01 Jan 0001 00:00:00 +0000

A non-technical guide to AI agents and the emerging protocol standards for product leaders and executives. Learn what agents are, why they matter for your product, and how the industry is standardizing agent communication through protocols like MCP, A2A, and AG-UI.

By the end of this path, you’ll understand the difference between chatbots and agents, how agents connect to external tools and data, and what enterprise agent deployment looks like. You’ll be equipped to evaluate whether your product needs agent capabilities and what infrastructure choices your team should be making.

Working With Messages

Mon, 01 Jan 0001 00:00:00 +0000

The Messages API is the core primitive — understand message roles, content blocks, and conversation structure.

AI Safety & Risk for Decision-Makers

Mon, 01 Jan 0001 00:00:00 +0000

A concise path for executives, product leaders, and board members who need to understand AI risks and safety without technical depth. Covers data privacy, hallucination risk, safety engineering principles, regulatory compliance (EU AI Act), and responsible AI governance.

By the end of this 2-hour path, you’ll understand the key risks of deploying AI in production, what questions to ask your engineering team about safety measures, and how to establish an AI governance framework for your organization.

📚 Core Concepts

Mon, 01 Jan 0001 00:00:00 +0000

🚀 Get Started

Mon, 01 Jan 0001 00:00:00 +0000

01 Intro Basics

Mon, 01 Jan 0001 00:00:00 +0000

Learn the basics of fine-tuning LLMs with Mistral AI’s API and open-source tools for optimized performance

Access management

Mon, 01 Jan 0001 00:00:00 +0000

Add Tools to Agents

Mon, 01 Jan 0001 00:00:00 +0000

Add tool calling capabilities to agents with automatic schema inference from Python functions.

AG-UI Overview

Mon, 01 Jan 0001 00:00:00 +0000

Agent Evaluation Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Agents

Mon, 01 Jan 0001 00:00:00 +0000

Learn about agents in the Agent User Interaction Protocol

Agents Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to Mistral’s agent system — autonomous task execution with tools, state persistence, connectors (code interpreter, web search), and multi-agent collaboration.

An Overview of Cohere's Models

Mon, 01 Jan 0001 00:00:00 +0000

Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case.

An Overview of Cohere's Rerank Model

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how Cohere’s Rerank models work.

An Overview of The Cohere Platform

Mon, 01 Jan 0001 00:00:00 +0000

Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications.

An Overview of the Developer Playground

Mon, 01 Jan 0001 00:00:00 +0000

The Cohere Playground is a powerful visual interface for testing Cohere’s generation and embedding language models without coding.

An Overview of Tool Use with Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Learn when to use leverage multi-step tool use in your workflows.

Architecture

Mon, 01 Jan 0001 00:00:00 +0000

Chroma is designed with a modular architecture that prioritizes performance and ease of use. It scales seamlessly from local development to large-scale production, while exposing a consistent API across all deployment modes.

Architecture overview

Mon, 01 Jan 0001 00:00:00 +0000

The most important mental model here is the three-layer architecture: Host (the application), Client (the protocol handler), and Server (the capability provider). A common mistake is conflating the host and the client – the host is the user-facing application (like Claude Desktop), while the client is an internal protocol component that maintains a 1:1 connection with a single server. Understanding this separation is essential before reading the specification or building anything.

Architecture Overview

Mon, 01 Jan 0001 00:00:00 +0000

How Chroma is structured across local, single-node, and distributed deployments.

Async

Mon, 01 Jan 0001 00:00:00 +0000

Audio

Mon, 01 Jan 0001 00:00:00 +0000

Authorization Extensions

Mon, 01 Jan 0001 00:00:00 +0000

Supplementary authorization mechanisms for the Model Context Protocol

Backups overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn about backups of serverless indexes in Pinecone.

Basic usage of tool use (function calling)

Mon, 01 Jan 0001 00:00:00 +0000

Overview of Cohere’s tool use system for building agentic workflows — with native citation support that links tool results back to generated text.

Basics

Mon, 01 Jan 0001 00:00:00 +0000

Understand the reinforcement learning fundamentals behind RFT

Bienvenue to Mistral AI Documentation

Mon, 01 Jan 0001 00:00:00 +0000

Mistral AI offers open-source and commercial LLMs, APIs, and tools for developers and enterprises to build AI-powered applications

Build a SQL Agent with Cohere's LLM Platform

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a tutorial on how to build a SQL agent with Cohere’s LLM platform.

Build an evaluation

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to build an evaluation pipeline with Weave Models and Evaluations

Build an MCP App

Mon, 01 Jan 0001 00:00:00 +0000

Getting started guide for building interactive UI applications with MCP Apps

Build applications

Mon, 01 Jan 0001 00:00:00 +0000

Build agentic applications utilizing compatible event AG-UI event streams

Build clients

Mon, 01 Jan 0001 00:00:00 +0000

Showcase: build a conversational CLI agent from scratch using AG-UI and Mastra

Build with Fireworks AI

Mon, 01 Jan 0001 00:00:00 +0000

Fast inference and fine-tuning for open source models

Build Your First Crew

Mon, 01 Jan 0001 00:00:00 +0000

Step-by-step tutorial to create a collaborative AI team that works together to solve complex problems.

Building a Creative Text-Based AI Game

Mon, 01 Jan 0001 00:00:00 +0000

Building AI Agents with DSPy

Mon, 01 Jan 0001 00:00:00 +0000

Building RAG as Agent

Mon, 01 Jan 0001 00:00:00 +0000

Cache

Mon, 01 Jan 0001 00:00:00 +0000

Chat

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to query our open-source chat models.

Choosing A Model

Mon, 01 Jan 0001 00:00:00 +0000

The essential decision guide for anyone evaluating which Claude model to use. Anthropic organizes its models into tiers — Haiku for speed and cost efficiency, Sonnet for the best balance of capability and price, and Opus for maximum intelligence on complex tasks. For product leaders, the key insight is that model selection is a business decision, not just a technical one: choosing Haiku over Opus can reduce costs by 10-20x while still handling most routine tasks. Compare this tiered approach with OpenAI’s model lineup (GPT-4o, GPT-4o mini, o1) and Mistral’s range to understand how the industry structures the speed-cost-quality tradeoff.

Chroma Cloud

Mon, 01 Jan 0001 00:00:00 +0000

Classification

Mon, 01 Jan 0001 00:00:00 +0000

Claude Code overview

Mon, 01 Jan 0001 00:00:00 +0000

Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools. Available in your terminal, IDE, desktop app, and browser.

CLI quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Cline with W&B Inference

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to configure the Cline coding agent to use W&B Inference.

Cloud

Mon, 01 Jan 0001 00:00:00 +0000

Access Mistral AI models via Azure, AWS, Google Cloud, Snowflake, IBM, and Outscale using cloud credits

Code Generation for Unfamiliar Libraries

Mon, 01 Jan 0001 00:00:00 +0000

Cohere Text Generation Tutorial

Mon, 01 Jan 0001 00:00:00 +0000

This page walks through how Cohere’s generation models work and how to use them.

Cohere's Command A Vision Model

Mon, 01 Jan 0001 00:00:00 +0000

Command A Vision is a powerful visual language model capable of interacting with image inputs. This document contains information about its capabilities.

Concepts

Mon, 01 Jan 0001 00:00:00 +0000

Understand concepts in Pinecone and how they relate to each other.

Connectors Overview

Mon, 01 Jan 0001 00:00:00 +0000

Connectors enable Agents and users to access tools like websearch, code interpreter, image generation, and document library on demand

Context overview

Mon, 01 Jan 0001 00:00:00 +0000

Context overview

Mon, 01 Jan 0001 00:00:00 +0000

Context snippets overview

Mon, 01 Jan 0001 00:00:00 +0000

Retrieve context snippets from your assistant’s knowledge base.

Contribute

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to contribute to Mistral AI through docs, code, community, and the Ambassador Program

Contributing

Mon, 01 Jan 0001 00:00:00 +0000

Contributing

Mon, 01 Jan 0001 00:00:00 +0000

Core capabilities overview

Mon, 01 Jan 0001 00:00:00 +0000

Overview of Agent Server core capabilities including streaming, human-in-the-loop, MCP, A2A, distributed tracing, webhooks, and double-texting.

Core Concepts

Mon, 01 Jan 0001 00:00:00 +0000

Counting tokens

Mon, 01 Jan 0001 00:00:00 +0000

Count input tokens precisely for text, images, files, and tools using the Responses API — essential for cost management and context window planning.

Courses

Mon, 01 Jan 0001 00:00:00 +0000

Standalone end-to-end examples showing how to use Fireworks to solve real-world use cases

Credits

Mon, 01 Jan 0001 00:00:00 +0000

Understanding credits and billing basics on Together AI.

CrewAI AMP

Mon, 01 Jan 0001 00:00:00 +0000

Deploy, monitor, and scale your AI agent workflows

CrewAI Cookbooks

Mon, 01 Jan 0001 00:00:00 +0000

Feature-focused quickstarts and notebooks for learning patterns fast.

Customer Support Chat

Mon, 01 Jan 0001 00:00:00 +0000

Building an AI-powered customer support chatbot with Claude, including architecture patterns and quality safeguards.

Data controls in the OpenAI platform

Mon, 01 Jan 0001 00:00:00 +0000

Your data is your data. An overview of how OpenAI uses your data, including retention and usage policies.

Data ingestion overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the different ways to ingest data into Pinecone.

Data retrieval with GPT Actions

Mon, 01 Jan 0001 00:00:00 +0000

Learn about performing data retrieval using APIs, relational databases, and vector databases with GPT Actions.

Deep Agents CLI

Mon, 01 Jan 0001 00:00:00 +0000

Terminal coding agent built on the Deep Agents SDK

Deep Agents CLI

Mon, 01 Jan 0001 00:00:00 +0000

Terminal coding agent built on the Deep Agents SDK

Deep Agents overview

Mon, 01 Jan 0001 00:00:00 +0000

Build agents that can plan, use subagents, and leverage file systems for complex tasks

Deep Agents overview

Mon, 01 Jan 0001 00:00:00 +0000

Build agents that can plan, use subagents, and leverage file systems for complex tasks

DeepSeek R1 Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

How to get the most out of reasoning models like DeepSeek-R1.

DeepSeek V3.1 QuickStart

Mon, 01 Jan 0001 00:00:00 +0000

How to get started with DeepSeek V3.1

Deploy your app to Cloud

Mon, 01 Jan 0001 00:00:00 +0000

Deployment Options - Overview

Mon, 01 Jan 0001 00:00:00 +0000

This page provides an overview of the available options for deploying Cohere’s models.

Deployment Options Overview

Mon, 01 Jan 0001 00:00:00 +0000

Compare Together AI’s deployment options: fully-managed cloud service vs. secure VPC deployment for enterprises.

Deployments Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Deploy models on dedicated GPUs in minutes

Deprecations

Mon, 01 Jan 0001 00:00:00 +0000

Learn about Cohere’s deprecation policies and recommended replacements

Developer quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use the OpenAI API to generate human-like responses to natural language prompts, analyze images with computer vision, use powerful built-in tools, and more.

Developing with Cursor

Mon, 01 Jan 0001 00:00:00 +0000

Use Cursor to build AG-UI implementations faster

Document AI

Mon, 01 Jan 0001 00:00:00 +0000

Mistral Document AI offers enterprise-grade OCR, structured data extraction, and multilingual support for fast, accurate document processing

Email Information Extraction

Mon, 01 Jan 0001 00:00:00 +0000

Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to get an embedding vector for a given text input.

Embeddings Overview

Mon, 01 Jan 0001 00:00:00 +0000

Mistral’s Embeddings API for text and code vector representations — supporting retrieval, clustering, and classification with open-weight models.

End-to-end RAG using Elasticsearch and Cohere

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a basic tutorial on how to get Cohere and ElasticSearch to work well together.

Enterprise deployment overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn how Claude Code can integrate with various third-party services and infrastructure to meet enterprise deployment requirements.

Entity Extraction

Mon, 01 Jan 0001 00:00:00 +0000

Error codes

Mon, 01 Jan 0001 00:00:00 +0000

An overview of error codes from the OpenAI API and Python library, including solutions and guidance.

Error Codes

Mon, 01 Jan 0001 00:00:00 +0000

Error Codes

Mon, 01 Jan 0001 00:00:00 +0000

An overview on error status codes, causes, and quick fix solutions

Evaluate a chatbot

Mon, 01 Jan 0001 00:00:00 +0000

Evaluate a prompt

Mon, 01 Jan 0001 00:00:00 +0000

Evaluate a RAG application

Mon, 01 Jan 0001 00:00:00 +0000

Evaluate a simple RAG system

Mon, 01 Jan 0001 00:00:00 +0000

This is the practical RAG evaluation walkthrough and the page most teams should run first when they need to measure a retrieval pipeline rather than guess at it. Pay attention to the distinction between retrieval metrics like context precision and recall and generation metrics like faithfulness and answer relevancy, because a RAG system can fail at either stage and the fix differs entirely. A common mistake is optimizing answer quality while ignoring context recall, leaving the model fluent but ungrounded. Start with the simple-evals page first if you are new to RAGAS.

Evaluate an AI Agent

Mon, 01 Jan 0001 00:00:00 +0000

Evaluate an AI Workflow

Mon, 01 Jan 0001 00:00:00 +0000

Evaluate RAG applications

Mon, 01 Jan 0001 00:00:00 +0000

Build and evaluate RAG applications using Weave with LLM judges

Evaluation concepts

Mon, 01 Jan 0001 00:00:00 +0000

Evaluation Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Evaluation overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn about evaluating the correctness and completeness of assistant responses.

Evaluation Overview

Mon, 01 Jan 0001 00:00:00 +0000

This is the foundational concept page for DSPy’s evaluate-then-optimize workflow, and it is essential reading before you touch any teleprompter. The key insight is that DSPy treats evaluation as a first-class input to compilation rather than an afterthought — your dev set and metric become the signal the optimizer uses to rewrite prompts. Start here, then move to the metrics page to define what good actually means for your task. Watch out for evaluating on the same examples you optimize against, which inflates scores and hides overfitting.

Evaluation quickstart

Mon, 01 Jan 0001 00:00:00 +0000

This is the fastest path into LangSmith evaluation and the right starting point before the deeper evaluator guides. The key takeaway is the dataset to target-function to evaluator to run loop, which is the mental model every other LangSmith eval feature builds on. Pay attention to how examples and the evaluation client are wired up, since that boilerplate carries over to LLM-as-judge work. A common beginner mistake is evaluating against a dataset that does not represent production traffic, which produces reassuring but meaningless scores.

Evaluations overview

Mon, 01 Jan 0001 00:00:00 +0000

Evaluation-driven LLM application development to systematically improve applications

Examples

Mon, 01 Jan 0001 00:00:00 +0000

Execution Hooks Overview

Mon, 01 Jan 0001 00:00:00 +0000

Understanding and using execution hooks in CrewAI for fine-grained control over agent operations

Experimental

Mon, 01 Jan 0001 00:00:00 +0000

Extend Claude Code

Mon, 01 Jan 0001 00:00:00 +0000

Understand when to use CLAUDE.md, Skills, subagents, hooks, MCP, and plugins.

Extensions Overview

Mon, 01 Jan 0001 00:00:00 +0000

Optional extensions to the Model Context Protocol

FAQs

Mon, 01 Jan 0001 00:00:00 +0000

Answers to common questions about Weave tracing

Files in Pinecone Assistant

Mon, 01 Jan 0001 00:00:00 +0000

Understand supported file types and metadata in Pinecone Assistant.

Financial Analysis with Yahoo Finance

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tuning Guide

Mon, 01 Jan 0001 00:00:00 +0000

Learn the basics and best practices of fine-tuning large language models.

Fireworks Agent Overview

Mon, 01 Jan 0001 00:00:00 +0000

Describe what you want, approve the plan and cost, get a deployed fine-tuned model.

Frequently Asked Questions About Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Cohere is a powerful platform for using Large Language Models (LLMs). This page covers FAQs related to functionality, pricing, troubleshooting, and more.

Functional API overview

Mon, 01 Jan 0001 00:00:00 +0000

Functional API overview

Mon, 01 Jan 0001 00:00:00 +0000

Generating llms.txt

Mon, 01 Jan 0001 00:00:00 +0000

GEPA for AIME (Math)

Mon, 01 Jan 0001 00:00:00 +0000

GEPA for Code Backdoor Classification (AI control)

Mon, 01 Jan 0001 00:00:00 +0000

GEPA for Privacy-Conscious Delegation

Mon, 01 Jan 0001 00:00:00 +0000

Get Started

Mon, 01 Jan 0001 00:00:00 +0000

DSPy overview and quick start guide

Get started with Studio

Mon, 01 Jan 0001 00:00:00 +0000

Get started with the desktop app

Mon, 01 Jan 0001 00:00:00 +0000

Install Claude Code on desktop and start your first coding session

Get Started with W&B Models

Mon, 01 Jan 0001 00:00:00 +0000

Get Started with Weights & Biases

Mon, 01 Jan 0001 00:00:00 +0000

Choose the right W&B product for your use case and learn how to get started

Getting Started

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Chroma is an open-source search engine for AI. It comes with everything you need to get started built-in, and runs on your machine.

Getting Started

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This five-minute quickstart is the fastest way into DeepEval: install it, write a test case, pick a metric, and run deepeval test run, which feels like pytest for LLM outputs. The critical thing to set up first is an OPENAI_API_KEY, because nearly all DeepEval metrics are LLM-as-a-judge evaluators that call a model under the hood. If a run appears stuck, suspect rate limits or quota rather than a framework bug, the most common early gotcha. DeepEval covers similar ground to RAGAS but with a pytest-style assertion workflow; read the metrics introduction next.

Getting Started

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Quick start guide

Getting Started

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Getting Started Agents

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Getting Started Chatbots

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Getting Started Llm Arena

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Getting Started Mcp

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Getting Started Rag

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Getting Started with Basic Tool Use

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This page describes how to work with Cohere’s basic tool use functionality.

Getting started with datasets

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Introduction to evaluation datasets — the foundation for systematic AI testing and the first step in eval-driven development.

Getting started with GPT Actions

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Learn how to set up and test GPT actions from scratch with the OpenAI API.

Getting Started with Logprobs

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Learn how to return log probabilities for your output tokens & build better classifiers.

GLM-5 Quickstart

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How to get the most out of GLM-5 for reasoning and agentic tasks.

Glossary

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Glossary

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Glossary of key AI and LLM terms, including LLMs, text generation, tokens, MoE, RAG, fine-tuning, function calling, embeddings, and temperature

Go

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Go

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GPT Action authentication

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Learn about authentication options for GPT actions, including no authentication, API key, and OAuth methods.

GPT Actions

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Learn about GPT Actions for customizing ChatGPT and interacting with external applications via APIs.

GPU Clusters Overview

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High-performance GPU clusters for training, fine-tuning, and large-scale AI workloads

Graders

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Learn about graders used for evals and fine-tuning.

Graph API overview

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Graph API overview

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Grounded Summarization Using Command R

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This page contains a basic tutorial on how to do grounded summarization with Cohere’s models.

Guides Homepage

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Quickstarts and step-by-step guides for building with Together AI.

Hello World! Explore Language AI with Cohere

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This page contains a breakdown of some of what can be achieved with Cohere’s LLM platform.

How to set up a JavaScript application

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How to set up an application with pyproject.toml

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How to set up an application with requirements.txt

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Image Generation

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Generate high-quality images from text + image prompts.

Image Generation Prompt iteration

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Indexing overview

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Understand key concepts related to indexing data in Pinecone.

Install LangChain

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Install LangChain

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Install LangGraph

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Install LangGraph

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Installation

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A guide for installing the Cohere SDK, supported in 4 different languages – Python, TypeScript, Java, and Go.

Installation

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Get started with CrewAI - Install, configure, and build your first AI crew

Installation

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Installation instructions

Installation

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Installation

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Installing the CLI

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Install the Chroma CLI to run a local server, browse collections, and interact with Chroma Cloud.

Integrations

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Integrations

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Pinecone integrations enable you to build and deploy AI applications faster and more efficiently. Integrate Pinecone with your favorite frameworks, data sources, and infrastructure providers.

Intro to Retrieval

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Ground LLMs in your own data using retrieval-augmented generation.

Introduction

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Learn how to get started building an AG-UI integration

Introduction

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Chroma is an open-source search engine for AI. It comes with everything you need to get started built-in.

Introduction

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Build AI agent teams that work together to tackle complex tasks

Introduction

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Introduction

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Introduction

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Introduction

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Introduction

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Fireworks Training API — custom training loops with full Python control over objectives, while Fireworks handles distributed GPU infrastructure.

Introduction

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Introduction

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Deploy custom containers on Together’s managed GPU infrastructure with automatic scaling, job queues, and built-in observability.

Introduction Comparisons

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Introduction Design Philosophy

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Introduction to A2A

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Introduction to Aya Vision

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In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses.

Introduction to Cohere on Azure AI Foundry

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An introduction to Cohere on Azure AI Foundry, a fully managed service by Azure (API v2).

Introduction to Conversational Context: Session, State, and Memory

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Introduction to Embeddings at Cohere

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Embeddings transform text into numerical data, enabling language-agnostic similarity searches and efficient storage with compression.

Introduction to Evaluations

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Learn how to use introduction to evaluations with W&B Weave

Introduction to Text Generation at Cohere

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This page describes how a large language model generates textual output.

Introduction to Traces

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Learn how to use introduction to traces with W&B Weave

Japanese documentation

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Localized overview and quickstart for Japanese-speaking developers.

Java

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Java

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Java

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Judge Alignment Quickstart

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Keyboard shortcuts

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Learn about the keyboard shortcuts available in W&B.

Kimi K2 QuickStart

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How to get the most out of models like Kimi K2.

Kimi K2 Thinking QuickStart

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How to get the most out of reasoning models like Kimi K2 Thinking.

Kimi K2.5 Quickstart

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How to get the most out of Kimi’s new K2.5 model.

Knowledge

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What is knowledge in CrewAI and how to use it.

La Plateforme

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Mistral AI’s La Plateforme offers pay-as-you-go API access to its latest models with flexible deployment options

LangChain overview

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LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves

LangChain overview

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LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves

LangGraph overview

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Gain control with LangGraph to design agents that reliably handle complex tasks

LangGraph overview

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Gain control with LangGraph to design agents that reliably handle complex tasks

LangSmith shared responsibility model

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Overview of how LangChain and customers share security responsibilities for the LangSmith platform.

Leaderboard Quickstart

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Learn how to use leaderboard quickstart with W&B Weave

Learn

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Tutorials, conceptual guides, and resources to help you get started.

Learn

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Tutorials, conceptual guides, and resources to help you get started.

Learn How Cohere's Rerank Models Work

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This page contains a basic tutorial on how Cohere’s ReRank models work and how to use them.

Learn more about sweeps

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Collection of useful sources for Sweeps.

Learn Weave with W&B Inference

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Legal Summarization

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Using Claude to extract key information from legal documents and generate structured summaries.

Llama 4 Quickstart

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How to get the most out of the new Llama 4 models.

LlamaIndex Agent Evaluation Quickstart

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LLM Benchmarking Quickstart

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Manage secrets

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Overview of W&B secrets, how they work, and how to get started using them.

Managed Fine-Tuning Overview

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Fine-tune models with Fireworks-managed infrastructure — no custom code required.

Managing Conversation History

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Math Reasoning

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MCP Apps

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Interactive UI applications that render inside MCP hosts like Claude Desktop

MCP Servers as Tools in CrewAI

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Learn how to integrate MCP servers as tools in your CrewAI agents using the crewai-tools library.

Meeting minutes

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Create an automated meeting minutes generator with Whisper and GPT-4.

Memory overview

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Memory overview

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Memory-Enabled ReAct Agents

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Metrics Introduction

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This page introduces DeepEval’s fifty-plus metrics, each scored from 0 to 1 with reasoning, and it matters because choosing the right metrics is the whole game in LLM evaluation. The key discipline the docs push is restraint: use no more than about five metrics, roughly two or three generic plus one or two custom to your use case, so you prioritize what truly matters instead of drowning in numbers. Because the metrics are LLM-as-a-judge, expect real cost and some run-to-run variance. This parallels RAGAS’s metric suite; read getting-started first if you have not run an evaluation yet.

Middleware

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Connect to existing protocols, in process agents or custom solutions via AG-UI

Migration

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Migration guides for Chroma version upgrades and schema changes.

Model customization

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Learn how to customize LLMs for your application with system prompts, fine-tuning, and moderation layers

Model Deprecations

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Model Gallery

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Pinecone integrations enable you to build and deploy AI applications faster and more efficiently. Integrate Pinecone with your favorite frameworks, data sources, and infrastructure providers.

Model Lifecycle

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Learn about W&B Inference model lifecycle and retirement

Model selection

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Guide to selecting Mistral models based on performance, cost, and use case complexity.’ (99 characters)

Model selection

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How to choose the right OpenAI model by balancing accuracy, latency, and cost — the fundamental tradeoff triangle for every AI application.

Model weights

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Open-source pre-trained and instruction-tuned models with various licenses, download links, and usage guidelines

Models Benchmarks

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Mistral’s benchmarked models excel in reasoning, multilingual tasks, coding, and multimodal capabilities, outperforming competitors in key benchmarks

Models Overview

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Mistral offers open and premier models for various tasks, including text, code, audio, and multimodal processing

Moderation

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OpenAI’s free moderation endpoint for detecting harmful content across categories like hate, violence, self-harm, and sexual content in both text and images.

Multi-Hop RAG

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Multilingual Search with Cohere and Langchain

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This page contains a basic tutorial on how to do search across different languages with Cohere’s LLM platform.

Observability concepts

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Open Source

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Chroma is the open-source AI application database. Contribute to the project or learn about telemetry and privacy.

OpenAI GPT-OSS Quickstart

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Get started with OpenAI’s GPT-OSS, open-source reasoning model duo.

Optimization Overview

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Organizations overview

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Understand organization structure, projects, and billing.

Output Refinement

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Overview

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Draft changes being considered for the AG-UI protocol

Overview

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Client package overview

Overview

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Core concepts in the Agent User Interaction Protocol SDK

Overview

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Core concepts in the Agent User Interaction Protocol SDK

Overview

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Documentation for encoding Agent User Interaction Protocol events

Overview

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Overview

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Overview

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This is the essential reference for understanding the Claude model family and should be one of the first pages you read before building anything with the Anthropic API. Focus on the capability differences between model tiers (Haiku, Sonnet, Opus) as this directly impacts your cost, latency, and quality tradeoffs in production. Pay attention to context window sizes and maximum output token limits, since these constraints will shape your prompt design and chunking strategies. When comparing with OpenAI’s model lineup, note that Anthropic’s naming convention signals capability tier rather than generation number.

Overview

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Anthropic’s taxonomy of proven AI use cases with implementation guidance for each.

Overview

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This overview introduces the agent skills framework, which provides a structured way to package and distribute reusable capabilities that Claude-based agents can invoke. Skills sit between raw tool definitions and full agent architectures, giving you a composable middle layer for common tasks like code review, PR creation, or domain-specific analysis. Start here before reading the individual tool docs to understand how skills orchestrate multiple tools into cohesive workflows. The key architectural decision is which capabilities to implement as standalone tools versus packaged skills, and this doc provides the mental model for making that choice.

Overview

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Overview

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This overview establishes the conceptual framework for prompt engineering with Claude and should be read before diving into specific techniques or best practices. Focus on how Claude’s instruction-following behavior differs from other models – Claude tends to be more literal in its interpretation of prompts, which means precise wording matters more than with some competitors. The page introduces key concepts like system prompts, role assignment, and output formatting that are referenced throughout all other prompt engineering materials. After reading this, proceed to the best practices guide for actionable techniques you can apply immediately.

Overview

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Overview

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Overview

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Overview

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Learn how to build, customize, and optimize your CrewAI applications with comprehensive guides and tutorials

Overview

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Monitor, evaluate, and optimize your CrewAI agents with comprehensive observability tools

Overview

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Leverage AI services, generate images, process vision, and build intelligent systems

Overview

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Automate workflows and integrate with external platforms and services

Overview

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Interact with cloud services, storage systems, and cloud-based AI platforms

Overview

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Connect to databases, vector stores, and data warehouses for comprehensive data access

Overview

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Read, write, and search through various file formats with CrewAI’s document processing tools

Overview

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Connect CrewAI agents with external automations and managed AI services

Overview

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Perform web searches, find repositories, and research information across the internet

Overview

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Extract data from websites and automate browser interactions with powerful scraping tools

Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Train models using reinforcement learning in minutes

Overview

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Overview

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Overview

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Overview

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Overview

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Build UIs that display real-time subagent streams, task progress, and sandbox for Deep Agents

Overview

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Stream real-time updates from deep agent runs and subagent execution

Overview

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Build generative UIs with real-time streaming from LangChain agents

Overview

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Control and customize agent execution at every step

Overview

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Stream real-time updates from agent runs

Overview

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Render LangGraph agents to the frontend

Overview

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Build UIs that display real-time subagent streams, task progress, and sandbox for Deep Agents

Overview

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Stream real-time updates from deep agent runs and subagent execution

Overview

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Build generative UIs with real-time streaming from LangChain agents

Overview

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Control and customize agent execution at every step

Overview

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Stream real-time updates from agent runs

Overview

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Render LangGraph agents to the frontend

Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Welcome to Together AI’s docs! Together makes it easy to run, finetune, and train open source AI models with transparency and privacy.

Overview

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Learn how to create configuration files for sweeps.

Overview

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Comprehensive API documentation and SDK references for Weights & Biases Weave

Overview of OpenAI Crawlers

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Parameter Settings

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Part 1. Intro to streaming

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Pinecone .NET SDK

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Pinecone Assistant

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Pinecone Assistant is a service that allow you to build production-grade chat and agent-based applications quickly.

Pinecone Assistant: n8n quickstart

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Create an n8n workflow to chat with documents using Pinecone Assistant and OpenAI.

Pinecone Assistant: SDK quickstart

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Use a Pinecone SDK to create an assistant, upload documents, and chat with the assistant.

Pinecone documentation

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Pinecone is the leading vector database for building accurate and performant AI applications at scale in production.

Pinecone Go SDK

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Pinecone Java SDK

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone Node.js SDK

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone Python SDK

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone Rust SDK

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Platform Overview

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Architecture, deployment lifecycle, and core concepts for Dedicated Container Inference.

Pondr, Fostering Connection through Good Conversation

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This page contains a basic tutorial on how tplay an AI-powered version of the icebreaking game ‘Pondr’.

Prerequisites

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Set up your environment to use W&B Inference

Prerequisites

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Set up your environment to use W&B Training

Pricing

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Understanding AI pricing is critical for any build-vs-buy decision. Anthropic charges per token (roughly per word) with different rates for input and output, and prices vary dramatically between model tiers — Haiku can be 50x cheaper than Opus per token. The practical implication: a customer support chatbot handling 10,000 conversations per day might cost $50/month with Haiku or $2,500/month with Opus, so model selection directly impacts unit economics. Pay attention to prompt caching and batch API discounts, which can cut costs by 50-90% for predictable workloads. Compare these structures with OpenAI and Mistral pricing to understand the competitive landscape before committing to a provider.

Pricing

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Privacy-Conscious Delegation

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Private Deployment Overview

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This page provides an overview of private deployments of Cohere’s models.

Program Of Thought

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Programming Overview

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DSPy takes a fundamentally different approach to working with LLMs: instead of manually writing and tweaking prompts, you define typed signatures (input/output schemas) and let DSPy’s optimizers compile them into effective prompts automatically. This is a paradigm shift from traditional prompt engineering, and it means the skills you build here are more about program design than wordsmithing. Focus on understanding Modules and Signatures first, as they are the core abstractions everything else builds on. A common gotcha is expecting DSPy to work well without a good set of training examples — the optimizers need representative input-output pairs to produce high-quality compiled prompts, so invest in your dataset before tuning the optimizer.

Projects overview

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Learn about projects, roles, and collaboration.

Prompt engineering concepts

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Prompt engineering quickstart

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Prompt Evaluation Quickstart

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Prompt Optimization Introduction

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Python

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Python

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Python

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Quick Start

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Quick Start

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Quickstart

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Welcome to Claude Code!

Quickstart

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This is the fastest path to a running agent and the best starting point before diving into advanced SDK features. Focus on the AgentConfig pattern, which is the central abstraction for defining agent behavior, tools, and instructions. Pay attention to how the SDK handles the agentic loop automatically – understanding what happens behind the scenes here will save you debugging time later. Read this before the Python or TypeScript language-specific guides so you have the conceptual model in place first.

Quickstart

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This quickstart walks you through building your first agent skill end-to-end, from definition to invocation within a Claude-powered agent. Focus on the skill registration pattern and how the runtime discovers and exposes skills to the model — getting this wiring right is foundational for everything else. Be aware that the quickstart uses simplified error handling; production skills need retry logic and graceful degradation as covered in the best practices guide. Work through this hands-on before reading the enterprise or best practices docs to build concrete intuition first.

Quickstart

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Build your first AI agent with CrewAI in under 5 minutes.

Quickstart

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Get a custom training loop running in minutes with the Fireworks Training API.

Quickstart

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Build an agent from a template

Quickstart

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Build an agent from a template

Quickstart

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Build your first deep agent in minutes

Quickstart

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Quickstart

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Quickstart

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Build your first deep agent in minutes

Quickstart

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Quickstart

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Quickstart

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Quickstart guide for setting up a Mistral AI account, configuring billing, and using the API for models and embeddings

Quickstart

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Step-by-step setup for installing the package, configuring API keys, and running your first agent locally.

Quickstart

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Build your first agent with the OpenAI Agents SDK, add tools and handoffs, and understand where to go next.

Quickstart

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Get started with Pinecone manually, with AI assistance, or with no-code tools.

Quickstart

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Deploy your first container in 20 minutes.

Quickstart

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Get up to speed with our API in one minute.

Quickstart

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Quickstart: Create Your First Cluster

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Get started with GPU Clusters in minutes

Quickstart: Flux Kontext

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Learn how to use Flux’s new in-context image generation models

Quickstart: Flux LoRA Inference

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Quickstart: FLUX.2

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Learn how to use FLUX.2, the next generation image model with advanced prompting capabilities

Quickstart: How to do OCR

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A step by step guide on how to do OCR with Together AI’s vision models with structured outputs

Quickstart: How to Use OpenClaw with Together AI

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Learn how to pair OpenClaw, a powerful autonomous agent, with frontier OSS models on Together AI like Kimi K2.5 and GLM 4.7.

Quickstart: Next.Js

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Build an app that can ask a single question or chat with an LLM using Next.js and Together AI.

Quickstart: Publish an MCP Server to the MCP Registry

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Publishing your MCP server to the registry makes it discoverable by any MCP client, which is a critical step for adoption beyond your own projects. Focus on the metadata requirements (name, description, capabilities) because poorly described servers are effectively invisible in the registry. Note that the registry currently requires GitHub-based authentication and a specific package manifest format — read through the validation errors carefully if your first publish attempt fails, as the error messages are quite specific. Come to this doc after you have a working server from the build-server tutorial, since you need a functional server before publishing makes sense.

Quickstart: Retrieval Augmented Generation (RAG)

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How to build a RAG workflow in under 5 mins!

Quickstart: Track LLM inputs & outputs

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Begin debugging LLM apps by adding tracing.

Quickstart: Using Hugging Face Inference With Together

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This guide will walk you through how to use Together models with Hugging Face Inference.

Quickstart: Using Mastra with Together AI

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This guide will walk you through how to use Together models with Mastra.

Quickstart: Using Vercel AI SDK With Together AI

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This guide will walk you through how to use Together models with the Vercel AI SDK.

Quickstart: Wan 2.7 T2V

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Generate videos from text prompts with optional audio input using the Wan 2.7 T2V model.

RAG With Chat Embed and Rerank via Pinecone

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This page contains a basic tutorial on how to build a RAG-powered chatbot.

Rate Limit

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Rate limit and usage tiers

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Learn about Mistral’s API rate limits, usage tiers, and how to upgrade for higher capacity.’ (99 characters)

Realtime quickstart

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Stand up low-latency realtime agents with websocket transport (WebRTC is not available in the Python SDK).

Reasoning

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Learn how to use reasoning models that think step-by-step before answering.

Reasoning Capabilities

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Reasoning models excel at tool use, agentic workflows, and complex problem-solving. This page provides a general overview of Cohere’s reasoning capalities.

Reference

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Reference

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Reference overview

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Generated documentation for W&B APIs

Remote Agent Quickstart

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Train an SVG drawing agent running in a remote environment

Rerank

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Learn how to improve the relevance of your search and RAG systems with reranking.

Reranking - quickstart

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A quickstart guide for performing reranking with Cohere’s Reranking models (v2 API).

Retrieval augmented generation (RAG) - quickstart

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A quickstart guide for performing retrieval augmented generation (RAG) with Cohere’s Command models (v2 API).

Retrieval evaluation using LLM-as-a-judge via Pydantic AI

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This page contains a tutorial on how to evaluate retrieval systems using LLMs as judges via Pydantic AI.

Retrieval-Augmented Generation (RAG)

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RL for Multi-Hop Research

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RL for Privacy-Conscious Delegation

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Run states

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Learn about the different states a W&B run can have.

Run your first experiment

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Runs

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An overview of runs in Agent Server, including how to kick off background runs, stateless runs, and cancel runs.

Sandboxes overview

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Use managed sandboxes to safely execute code and interact with the filesystem in isolated environments.

Saving and Loading

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Schema Basics

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Learn how to create and use Schema to configure indexes on your Chroma collections.

Schema Overview

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Scoring Overview

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Evaluate AI outputs and return evaluation metrics with Weave Scorers

SDK Clients

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Official Python & TypeScript SDKs and community clients for Mistral AI

Search API Overview

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Search Basics

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Learn how to construct and use the Search class for querying your Chroma collections.

Search overview

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Explore semantic, lexical, and hybrid search options.

Security overview

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Understand Pinecone’s security features, including authentication, encryption, and audit logs.

Security overview

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Understand Pinecone’s security features, including authentication, encryption, and audit logs.

Self-deployment

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Deploy Mistral AI models on your infrastructure using vLLM, TensorRT-LLM, TGI, or tools like SkyPilot and Cerebrium

Semantic search - quickstart

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A quickstart guide for performing text semantic search with Cohere’s Embed models (v2 API).

Semantic Search with Cohere Embed Jobs

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This page contains a basic tutorial on how to use Cohere’s Embed Jobs functionality.

Server

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Implement AG-UI compatible servers

Serverless Overview

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How Serverless inference works on Fireworks: serving paths, billing, request/response headers, prompt caching, model lifecycle, and when to choose Serverless over On-demand

Serverless Quickstart

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Make your first Serverless API call in minutes

Serverless Semantic Search with Cohere and Pinecone

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This page contains a basic tutorial on how to get Cohere and the Pinecone vector database to work well together.

Setup

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Single-Turn Training Quickstart

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Train a model to be an expert at answering GSM8K math questions

SQL Agent with Cohere and LangChain (i-5O Case Study)

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This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry.

Streaming

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Structured Output

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Learn to generate structured outputs like JSON for LLM agents and pipelines, with custom and flexible formatting options

Structured Outputs for LLMs

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Introduction to structured outputs with LLMs

Synthetic Data Generation Introduction

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Technical Overview

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Test Pinecone at scale

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Test Pinecone with a real-world dataset and semantic search workload.

Text generation - quickstart

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A quickstart guide for performing text generation with Cohere’s Command models (v2 API).

Text-to-SQL Evaluation Quickstart

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The Cohere Datasets API (and How to Use It)

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Learn about the Dataset API, including its file size limits, data retention, creation, validation, metadata, and more, with provided code snippets.

The Cookbook

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Ready-to-run training recipes for GRPO, DPO, SFT, and distillation built on top of the Training API.

Thinking in LangGraph

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Learn how to think about building agents with LangGraph

Ticket Routing

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Using Claude to automatically classify and route support tickets to the right team based on content analysis.

Token Usage

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Tokenization

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Learn about Mistral AI’s tokenization process, including subword tokenization, control tokens, and Python implementation for LLMs

Tool use & agents - quickstart

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A quickstart guide for using tool use and building agents with Cohere’s Command models (v2 API).

Tools Overview

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Discover CrewAI’s extensive library of 40+ tools to supercharge your AI agents

Trace an LLM application tutorial

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Tracing Basics

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Track and monitor your AI application’s execution with Weave tracing

Tracing quickstart

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Track Application Logic

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Learn how to track data flow and metadata in your LLM applications

Training Overview

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Launch RFT jobs using the eval-protocol CLI

Training Overview

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This overview frames Fireworks’ three fine-tuning paths — the autonomous Agent, semi-managed Managed Fine-Tuning, and the custom Training API — so it matters as the decision page before you commit compute. The key heuristic it offers is to reach for supervised fine-tuning when you have more than about a thousand quality labeled examples, and to switch to reinforcement fine-tuning for smaller datasets or reasoning-heavy tasks where ground-truth labels do not exist. A common mistake is defaulting to SFT on too little data. This is the Fireworks counterpart to Together AI’s fine-tuning flow; read the quickstart first if you are new to the platform.

Triggers Overview

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Understand how CrewAI AMP triggers work, how to manage them, and where to find integration-specific playbooks

Troubleshooting

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Common issues and solutions when working with Chroma.

Tutorial Introduction

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Tutorial: App versioning

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Learn how to use Weave Model to track and version your application and its parameters

Tutorial: Create sweep job from project

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Tutorial on how to create sweep jobs from a pre-existing W&B project.

Tutorial: Create, track, and use a dataset artifact

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Create, track, and use a dataset artifact with W&B.

Tutorial: Define, initialize, and run a sweep

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Sweeps quickstart shows how to define, initialize, and run a sweep. There are four main steps

Tutorial: Log tables, visualize and query data

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Explore how to use W&B Tables with this 5 minute Quickstart.

Tutorials

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Tutorials Overview

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Understanding projects

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Learn about projects, environments, and member roles.

Use MCP in DSPy

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Using GPT-5.2

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Learn about how to use and migrate to GPT-5.2 and the GPT-5 model family, the latest models in the OpenAI API.

Vibe Coder Quickstart

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Video Generation

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Generate high-quality videos from text and image prompts.

View runs in a project

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Details about customizing how runs are displayed in your project’s runs table

Vision LLMs

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Learn how to use the vision models supported by Together AI.

Voice activity detection (VAD)

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Learn about automatic voice activity detection in the Realtime API.

Voice quickstart

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Build speech-enabled agents with streaming transcription and TTS.

W&B Quickstart

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W&B Quickstart

W&B Self-Managed deployment overview

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Deploy W&B Self-Managed on cloud or on-premises infrastructure

W&B Tutorials & Blog

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Web QA with embeddings

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How to build an AI that can answer questions about your website.

What is the Model Context Protocol (MCP)?

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This is the single best starting point for understanding MCP. Focus on the core value proposition: MCP standardizes how AI applications connect to external data and tools, replacing fragile one-off integrations with a shared protocol. Pay attention to the analogy with USB-C — MCP aims to be a universal connector between LLMs and the systems they need to interact with. The original page also covers building MCP Apps — interactive apps that run inside AI clients — via the MCP Apps overview. Read this before diving into architecture or specification pages.

What is Weave?

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Learn about W&B Weave and how it helps you build, evaluate, and improve LLM applications

Whats New Claude 4 6

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Workflow Evaluation Quickstart

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Your First Agent

Mon, 01 Jan 0001 00:00:00 +0000

Step-by-step tutorial to build your first agent with the Microsoft Agent Framework.