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Hectal
Back to the lesson: Topic 13.1 — The RAG Interview Map
RAG and Vector Search · Example 1 of 1

The interview map as a study list

Every pointer is a topic of this course. Add your own weak questions to the list and drill the areas with most rows.

RAG interviews probe five areas: fundamentals, design, debugging, trade-offs and production. This topic maps typical questions to the topic of this course that answers each, and gives a method for any answer.

Change the code and press Run (Ctrl+Enter). Predict what will happen first, then break it on purpose and read what changes. Your edits are saved and match the lesson page.

Ready for more? This phase has 5 hands-on exercises with automatic checks.

Practice questions

Write the code in the editor, run it, then open the model answer to compare.

01

Write by_area(questions, area) that returns the topic numbers of the rows in area, in order. Test it on a list of three rows and print the result for 'debugging'.

02

Write missing_areas(questions, practised) that returns the sorted list of areas present in questions but not in the set practised. Print it for a practised set of two areas.

Explain it without notes

01

Name the five areas of RAG interview questions and what each one tests.

02

What is the method for structuring any answer, and why does it start with a clarifying question?

03

Why is 'try a better model' a weak answer to 'the answers are wrong'?

04

How do you prepare for questions you cannot predict?

The interview map as a study list
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Expected output

fundamentals (3)
  Topic 1.4: Why does BM25 beat raw word counts?
  Topic 2.2: Cosine, dot product or distance: does it matter?
  Topic 2.4: What does an embedding model actually learn?
design (3)
  Topic 4.4: How would you choose a chunk size?
  Topic 3.4: Walk me through a vector index such as HNSW.
  Topic 10.4: How do you keep one customer's data from another's?
debugging (2)
  Topic 9.6: The answers are wrong. Where do you start?
  Topic 9.7: Last week's answers were better. What changed?
trade-offs (2)
  Topic 6.7: Hybrid search or vectors only?
  Topic 12.8: RAG or a long context window?
production (2)
  Topic 11.2: How fresh can answers be, and how do you prove it?
  Topic 11.5: What does a question cost, and what is the biggest lever?
total: 12 questions in 5 areas