B4Beginner 75 minutes reading and codeLocked

Embeddings & Vector Representations

Text is discrete. Math is continuous. Every time you ask an LLM to find "similar" documents, compare meanings, or search beyond keywords, you're relying on a bridge between these two worlds. That bridge is an embedding. If you don't understand embeddings, you don't understand modern AI. You just use it.

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 3500 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.

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Milestone forThe ZRA Tax Desk

Embed the ZRA library

Parse at least 30 ZRA guides, chunk by section keeping document, page and heading, and embed them. Try two embedding models and two chunk sizes; measure which finds the right section for 20 questions.

Done when

  • An ingestion script that rebuilds the index from scratch in one command
  • Every chunk carries document, page and heading
  • Two models × two chunk sizes compared: recall@5 on 20 questions, best setting at least 0.75

Stretch: Add a duplicate-detection pass for guides that repeat each other

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.

B3Structured Outputs: JSON, Schema Validation, Constrained DecodingNext · B5Context Engineering: Windows, Budgets, Memory, and Retrieval

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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