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Lesson 1 of 24 · 20 min

Why RAG gets hard

From five notes to a whole handbook

RAG has three steps: retrieve the parts of your documents that match a question, augment the prompt with them, and let the model generate an answer from them. With five short notes, almost anything works. With real documents, each step can quietly go wrong:

ProblemWhat happensYou'll fix it in
Messy text from PDFsOdd characters, page headers and "Page 3 of 9" mixed into the contentModule 1
Bad chunksA rule cut in half, or a chunk about three different thingsModule 2
Old versionsThe 2024 handbook says pay day is Thursday; the 2026 one says WednesdayModule 2
Different words"How long is lunch?" vs "a 30-minute unpaid meal break"Module 3
Exact names and codes"KeyPay", "FSS-208841" — meaning search can miss themModule 4
No proof it worksIt seems fine on the three questions you triedModule 4
Made-up answersThe model fills gaps, or mixes facts upModule 5

The big lesson of this course: RAG quality is mostly about retrieval. If the right text reaches the model, even a small model answers well. If it doesn't, no model can.

What you'll build

The Corner Café Staff Handbook Assistant. Staff ask things like "How do I swap a shift?" or "A customer has a nut allergy — what do I do?", and get a short answer with numbered sources that link to the exact page of the right document. It searches seven documents:

  • The Staff Handbook 2026 (a 9-page PDF with a pay-rates table) — and the old 2024 handbook, which disagrees with it
  • The Food Safety Program and the Espresso Machine Guide (PDFs with tables)
  • The Refunds and Complaints and Privacy policies (Word files)
  • A Staff FAQ (Markdown)

They're made up, but written like the real thing — and the techniques work the same on your workplace's documents.

Try it

  1. Think of the documents at a job you know: where are they kept, how many are there, and what questions do people ask about them?
  2. Download the course files from this lesson.

Downloads

1 file to download once you enrol.