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How should I learn retrieval augmented generation?

Short answer from Learnetto's Best RAG courses for developers guide.

Short answer

Learn ingestion, chunking, embeddings, metadata, retrieval, reranking, prompt construction, citations, and evaluation in that order. RAG quality usually fails before the final generation step.

Context from the full guide

Start with a basic retrieval or vector-search course if RAG is new, then use Building and Evaluating Advanced RAG Applications when you need better retrieval quality, evaluation, and production patterns.

Read the full guide

Useful resources

  1. Building and Evaluating Advanced RAG Applications

    Short course · DeepLearning.AI · Intermediate

    You already know basic RAG and need better retrieval, evaluation, and production-quality patterns.

  2. Pinecone Learn: Retrieval-Augmented Generation

    Guide · Pinecone · Beginner to intermediate

    You need to understand the moving parts of RAG.

  3. OpenAI Retrieval guide

    Guide · OpenAI · Intermediate

    You need the official path for file search, retrieval, and grounded answers before designing a RAG stack.

  4. OpenAI Cookbook

    GitHub repo · OpenAI · Beginner to advanced

    You need implementation examples rather than theory.

  5. Prompt Engineering Guide

    Guide · DAIR.AI · Beginner to advanced

    You want examples of prompting techniques and patterns.

  6. Vercel AI SDK Tutorial

    Free tutorial · Matt Pocock · Beginner to intermediate

    You want to build TypeScript LLM apps with Vercel's AI SDK, including streaming, structured outputs, model switching, embeddings, tool calls, and agents.

  7. handoff: Move Context Between Agent Sessions

    Guide / Claude skill · Matt Pocock · Intermediate

    You need to preserve useful context across agent sessions without dragging an overloaded conversation forward.

  8. LangChain for LLM Application Development

    Short course · DeepLearning.AI · Beginner to intermediate

    You want a fast introduction to building LLM applications with chains, retrieval, and tools.

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