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Build a small in-memory document indexer and retriever from scratch (inverted index + BM25), then add a vector option.

A bridge between classic DSA and modern RAG. The signal is building a working inverted index and a correct BM25 score by hand, reasoning about its complexity, and then knowing exactly when you would reach for embeddings and an ANN index instead.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

A bridge between classic DSA and modern RAG. The signal is building a working inverted index and a correct BM25 score by hand, reasoning about its complexity, and then knowing exactly when you would reach for embeddings and an ANN index instead.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·RetrievalFree240mProject: build a retrieval system over your own documentsThe first project of the course. Build a working question-answering system over documents you actually have, in five stages, each one checkable on its own, and finish with something honest enough to put in a portfolio.
UP NEXT ON YOUR JOURNEY
Next in this trackHow do you operate a multi-vector (ColBERT-style) index in production without it blowing up storage?Next in this trackHow do you decompose a complex query into sub-queries for retrieval, and when does it backfire?Next in this trackWhen does HyDE hurt retrieval, and what variants fix its failure modes?
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