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Learning roadmap

Data & Vector Foundations

The layer under every good RAG system: embeddings that mean something, chunking that respects structure, vector stores you can query honestly, and retrieval quality you can measure before users measure it for you.

4 stages~8 h
That leaves 4 stages, ~8 hours of focused work. You can clear it in any order, but the stages compound — try them top-to-bottom your first time.
  1. You'll pick up:

    embedding space, cosine similarity, paraphrase clustering, domain mismatch, bias in vectors

    Learn this stage with Sensei
    Practice quizzes
    Lab task

    Build a "similarity playground": a text box where two inputs produce vectors shown as a 2-D projection with a similarity score, plus a "trick pair" button loading cases where similarity misleads (opposites, negations, cross-domain).

    Build it in the Lab
    Checkpoint

    You can predict which query-document pairs will match before running them — and name one case where the model will be confidently wrong.

Start the roadmap: Embeddings: intuition before math →
What you'll be able to do
  • Explain to a teammate why two sentences with no shared words are "similar" — and when that similarity lies.
  • Chunk a real document set without shredding its meaning, metadata intact.
  • Stand up a vector store with hybrid search and justify the index choice.
  • Report retrieval quality with recall@k on a golden set — not with adjectives.
Or try a related path