Learn Retrieval-Augmented Generation (RAG)

Connect language models to useful, traceable evidence.

Start learning 6 lessons · about 1 hours · free
About this track

Learn how retrieval-augmented generation systems prepare knowledge, find relevant passages, and produce evidence-grounded answers. Explore chunking, embeddings, ranking, citations, evaluation, and security through practical examples.

Before you start
  • No prior RAG experience needed.
  • Basic Python is helpful but not required.
  • A modern browser with JavaScript enabled.
  1. Unit 1 · 0/2 lessons

    RAG foundations

    Trace the pipeline and prepare a clean, traceable corpus. Badge: Corpus curator

    1. 0x001Understand the RAG pipelineTrace a question through retrieval and generation, and see what each stage contributes. 12 min 12 min
    2. 0x102Prepare and chunk source documentsTurn source files into clean, traceable passages suitable for search. 12 min 12 min
  2. Unit 2 · 0/2 lessons

    Retrieval systems

    Represent, search, filter, and rerank evidence. Badge: Evidence finder

    1. 0x203Represent meaning for retrievalLearn how embeddings and indexes support semantic search. 12 min 12 min
    2. 0x304Retrieve, filter, and rerank evidenceBuild a candidate set and improve its ordering before generation. 12 min 12 min
  3. Unit 3 · 0/2 lessons

    Grounding and quality

    Generate supported answers and evaluate system quality safely. Badge: Grounded guide

    1. 0x405Generate answers grounded in sourcesGive the model clear instructions and make evidence visible in its response. 12 min 12 min
    2. 0x506Evaluate and secure a RAG systemMeasure retrieval and answer quality while protecting sensitive knowledge. 12 min 12 min
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