AI Search / RAG Engineer — New-Grad Edition

1. Start Here

   Is This Job For You?

   A five-minute orientation before the real chapters: what an AI Search / RAG engineer actually does, whether it fits you, and how to get everything…

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2. 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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3. 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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4. 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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5. 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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6. 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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7. 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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8. 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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9. 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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10. 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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11. 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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12. 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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13. 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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14. Career

    Landing Your First RAG Role

    You've learned the pipeline and built a harness. Here's how to turn that into a job.

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15. Appendix

    Interview Simulation

    Ten questions you will actually be asked, each with what the interviewer is really probing, a strong answer built from this book, and the weak…

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AI Search / RAG Engineer — New-Grad Edition · AI Engineer Dojo · Other editions