Evaluation & Testing

Steps

1. Getting started with datasets openai beginner

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

   Start with OpenAI's primer on evaluation concepts — datasets, metrics, and the eval lifecycle. This establishes the vocabulary and mental model you'll use across all providers: define what good looks like, measure it, iterate.

2. Evaluations overview wandb beginner

   Evaluation-driven LLM application development to systematically improve applications

   Weave approaches evaluation as a first-class development primitive, not an afterthought. Understanding evaluations as core types alongside models and datasets is a mindset shift — your eval suite should evolve with your application.

3. Evaluation Overview dspy beginner

   DSPy's evaluation system is deeply integrated with its optimization loop — metrics drive automatic prompt tuning. This is fundamentally different from the 'test after build' approach of other frameworks. Compare with how OpenAI and W&B treat evals.

4. Working with evals openai intermediate

   Build, run, and iterate on evaluations to systematically test and improve AI model outputs — OpenAI's practical guide to eval-driven development.

   The practical implementation guide for building and running evaluations with OpenAI. Focus on how to structure evaluation datasets, choose grading criteria, and interpret results. This is where concepts from step 1 become code.

5. Evaluation quickstart langchain beginner

   LangSmith provides evaluation infrastructure that works across LLM providers. The key value is traceability — you can see exactly which retrieval step or prompt caused a failure, not just that the final answer was wrong.

6. Evaluate a simple LLM application ragas intermediate

   RAGAS is purpose-built for evaluating RAG pipelines with metrics like faithfulness, answer relevancy, and context precision. If you're building RAG applications, these domain-specific metrics catch failures that generic evals miss.

7. How to define an LLM-as-a-judge evaluator langchain intermediate

   Using an LLM to evaluate LLM outputs is powerful but has subtle pitfalls — bias, inconsistency, and circular reasoning. LangSmith's guide covers how to design reliable LLM judges with rubrics and few-shot examples.

8. Build an evaluation wandb beginner

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

   Hands-on tutorial for building a complete evaluation pipeline with Weave. The practical focus on scorers, datasets, and result visualization shows the full workflow from writing eval code to analyzing results.

9. Metrics dspy intermediate

   DSPy's metric system goes beyond pass/fail — metrics can be continuous, multi-dimensional, and used as optimization objectives. Understanding metric design here informs better eval practices across any framework.

10. Evaluation best practices openai advanced

    Advanced evaluation patterns for production AI systems — handling ambiguous cases, scaling eval suites, avoiding eval gaming, and integrating evals into CI/CD pipelines.

    Advanced evaluation patterns: handling ambiguous cases, scaling eval suites, avoiding eval gaming, and using evals in CI/CD. These production concerns separate toy evaluations from reliable quality gates.

11. Evaluate a simple RAG system ragas beginner

    Apply RAGAS metrics to evaluate a real RAG system end-to-end. This practical tutorial connects the metrics concepts from step 6 to actual pipeline evaluation — a template you can adapt for your own RAG applications.

12. Agent evals openai intermediate

    Use agent evals to create datasets, configure graders, and track evaluation runs for your agents.

    Evaluating agents is harder than evaluating single-turn LLM calls — agents have multi-step trajectories, tool use sequences, and state management. This guide covers the unique challenges and patterns for agentic evaluation.