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Working engineers · The AI Search / RAG Engineer

The AI Search / RAG Engineer

Build retrieval that makes language models tell the truth about your own documents — and prove it with numbers.

  1. Ch 1

    Why Search Came Back

    Everyone said large language models would kill search. The opposite happened: retrieval became load-bearing infrastructure inside the model's loop.…

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  2. Ch 2

    Anatomy of a RAG Pipeline

    "The answer was wrong" is a symptom, not a diagnosis. A RAG system is five stages in a line, and a bad answer traces to exactly one of them. This…

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  3. Ch 3

    Ingest & Chunking

    Chunking is the least glamorous stage and the one that quietly caps your ceiling. Get it wrong and no retriever, reranker, or model can recover —…

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  4. Ch 4

    Embeddings & Dense Retrieval

    An embedding turns text into a point in space where "near" means "means the same thing." That one idea powers semantic search — and it fails in…

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  5. Ch 5

    Lexical & Hybrid Retrieval

    Everyone reaches for embeddings first. But the retrievers that win in production almost always run two systems — a lexical one and a dense one — and…

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  6. Ch 6

    Reranking

    Retrieval's job is to not miss the answer — to get it somewhere in the top 50. Reranking's job is to get it to the top 3. They're different problems…

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  7. Ch 7

    Query Understanding & Transformation

    So far you've improved the index and the retriever. But half of retrieval failures are caused by the query — it's too short, too vague, or worded…

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  8. Ch 8

    Evaluating Retrieval

    Everything so far — chunking, hybrid, reranking, query transforms — is a knob. Without measurement, turning knobs is superstition. This chapter…

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  9. Ch 9

    Grounded Generation & Citations

    You retrieved the right chunks and ranked them on top. The model can still ignore them, blend them with its own training memory, or state a fact no…

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  10. Ch 10

    Production RAG

    A RAG system that scored well offline can rot in production without a single code change — because the corpus changes, the query distribution…

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  11. Ch 11

    Advanced Retrieval

    The vanilla "embed, retrieve top-k, generate" pipeline handles most queries. The ones it can't — filtered searches, multi-hop questions, queries…

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  12. Ch 12

    Tools of the Trade & Interview Prep

    You now have the mental models and the measurement discipline. This chapter maps them onto the real tools you'll name in an interview, then…

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The AI Search / RAG Engineer · AI Engineer Dojo · Other editions