B6Beginner 90 minutes reading and codeLocked

RAG (Retrieval-Augmented Generation)

Your LLM knows everything up to its training cutoff. It knows nothing about your company's docs, your codebase, or last week's meeting notes. RAG solves this by retrieving relevant documents and stuffing them into the prompt. It's the most deployed pattern in production AI. If you build one thing from this course, build a RAG pipeline.

In this lesson

  1. 01Learning Objectives
  2. 02The Problem
  3. 03The Concept
  4. 04Build it
  5. 05Use it
  6. 06Exercises
  7. 07Key Terms
  8. 08Further Reading

About 2900 words of reading, with code, 5 quiz questions and the tutor.

The full lesson opens with an access code

You can see what this lesson covers and the milestone it sets. The full text, code, quizzes, the tutor and the AI reviews come with the K850 practice, along with weekly build sessions and code review.

Codes come with the K850 practice. Book a place

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Milestone forThe ZRA Tax Desk

Answer from the documents, cite the page

Wire retrieval into generation. Answers must cite document and page; a post-check confirms every cited page was among the retrieved chunks and drops answers that cite anything else.

Done when

  • Answers cite document and page, checked by a post-filter
  • The post-filter rejects every invented citation in a 10-case test
  • Recall@5 at least 0.8 and citation correctness at least 0.9 on 30 questions

Stretch: Add a conversation mode and show follow-up questions keep their citations

Reviews open with an access code.

Interview practice

Questions on this topic that AI engineering interviews ask, with the companies reported to ask them. Answer the way you would out loud; Jev scores it and DeepSeek tells you what to add.

Scoring opens with an access code. You can still read the questions and prepare.

B5Context Engineering: Windows, Budgets, Memory, and RetrievalNext · B7Function Calling & Tool Use

Adapted from open-source work: AI Engineering from Scratch by Rohit Ghumare (MIT, lesson text, code and quizzes); Awesome LLM Apps by Shubham Saboo (Apache-2.0, starter code); AI Engineering Interview Questions by Outcome School (Apache-2.0, interview questions). Capstones, milestones, data packs and Zambian context by Zambrite.

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