capability~300 mindifficulty

Retrieval-Augmented Generation (RAG)

RAG lets an LLM answer accurately about your private or up-to-date data without retraining.

1

Learn it

Read Pinecone's RAG series to understand the architecture, chunking, and retrieval quality trade-offs. Then build a working pipeline with the Real Python LangChain RAG walkthrough, wiring together embeddings, a vector store, retrieval, and prompt assembly with source citations.

By the end you’ll be able to

  • Build the retrieve-then-generate pipeline end to end
  • Chunk documents sensibly and ground answers in cited sources

Free resources

2

Practice

Build a RAG pipeline over documents you care about that answers questions and cites which chunk each answer came from.

3

Build it — your output

A question-answering pipeline that returns grounded, source-cited answers over your own corpus.

4

Prove it — check yourself

You’re ready to move on when you can honestly say yes to each:

  • Can you describe the full retrieve-then-generate flow?
  • Can you tune chunk size and top-k and explain the effect on answers?
  • Can you show that answers are grounded in retrieved sources?

Do it with AI

Use a free AI tool as your tutor and pair-programmer. Copy a prompt below into Claude, ChatGPT, or Gemini (for building, try Cursor or Copilot).

The habit: Try it yourself first — a little struggle is where the learning actually happens. Then ask for a hint before the answer — never copy blindly.

  • Understand itClaude / ChatGPT / Gemini

    I'm a beginner learning "Retrieval-Augmented Generation (RAG)". Explain it in simple terms with a real-world analogy, then ask me ONE question to check my understanding — don't reveal the answer.

  • PracticeClaude / ChatGPT / Gemini

    Give me 3 short practice problems on "Retrieval-Augmented Generation (RAG)" (my goal: build the retrieve-then-generate pipeline end to end), from easy to hard. Don't include the solutions yet — I'll try them first, then ask you to check.

  • Get unstuckClaude / ChatGPT / Gemini

    I'm stuck on this exercise about "Retrieval-Augmented Generation (RAG)": [paste your attempt here]. Give me ONE small hint to move forward — do NOT give me the full solution.

  • Build & reviewCursor / Copilot

    Here is my work for a "Retrieval-Augmented Generation (RAG)" task: [paste your code or answer]. Review it: point out what's wrong and WHY, and suggest improvements — but let me make the fixes myself.

  • Prove itClaude / ChatGPT / Gemini

    Quiz me on "Retrieval-Augmented Generation (RAG)". Ask me 5 questions one at a time, wait for my answer each time, then tell me if I'm right and briefly why.

My notes