RAG from Scratch

Steps

1. Retrieval Augmented Generation (RAG) cohere intermediate

   Guide on using Cohere's Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations.

   Start with the conceptual foundation — RAG solves hallucination by grounding LLM responses in retrieved evidence. Cohere has native RAG support unlike most providers where you must orchestrate retrieval yourself with LangChain or similar.

2. Introduction to Embeddings at Cohere cohere beginner

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

   Embeddings are the bridge between text and vector search. Pay attention to Cohere's input type parameter (search_document vs search_query) and dimension choices — getting these right directly affects retrieval quality downstream.

3. Pinecone documentation pinecone beginner

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

   Pinecone is the vector database layer — it stores and queries the embeddings you generate with Cohere or OpenAI. Focus on index configuration, especially the dimension setting which must match your embedding model exactly.

4. Semantic Search with Cohere Models cohere intermediate

   This is a tutorial describing how to leverage Cohere's models for semantic search.

   Semantic search finds documents by meaning rather than keywords, and is the retrieval backbone of any RAG pipeline. Understand the two-stage pattern: embed your query, then search the vector index — you will combine this with reranking next.

5. Master Reranking with Cohere Models cohere intermediate

   This page contains a tutorial on using Cohere's ReRank models.

   Reranking is the highest-impact single improvement you can make to RAG quality. It reorders retrieved documents by relevance using a cross-encoder model, and works with any embedding provider — not just Cohere's own embeddings.

6. Retrieval augmented generation (RAG) - quickstart cohere beginner

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

   This ties together embeddings, search, and generation into a working RAG pipeline. Focus on how retrieved documents flow into the generation prompt — the chunking strategy you choose here directly affects both citation quality and response accuracy.

7. RAG Citations cohere intermediate

   Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for RAG. It covers both non-streaming and streaming modes (API v2).

   Citations ground RAG responses in their source material and are essential for production trustworthiness. Cohere's citation system provides span-level grounding back to specific chunks — more granular than most custom implementations offer.

8. Build a custom RAG agent with LangGraph langchain intermediate

   Agentic RAG uses tool-calling agents to decide when and how to retrieve, rather than always retrieving. This is the evolution from simple RAG — the agent can reformulate queries, retrieve from multiple sources, or skip retrieval entirely based on context.

9. Building Agentic RAG with Cohere cohere intermediate

   Hands-on tutorials on building agentic RAG applications with Cohere

   Cohere's take on agentic RAG integrates tool use with their native RAG pipeline. Compare this with the LangGraph approach in the previous step — Cohere handles more orchestration natively while LangGraph gives you full control over the agent graph.