RAG & Retrieval Engineering.

Production retrieval-augmented generation as a deep engineering discipline: chunking, embedding selection, query transformation, hybrid search, reranking, vector indexing/quantization, retrieval eval, agentic RAG, ingestion pipelines, and context assembly. The engineering-depth layer above ai_engineering.rag_architecture. Covers 12 sub-skills.

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  RAG Fit & Tradeoffs

  A team wants their assistant to adopt a specific brand tone and formatting style across all replies, not to learn new facts. Which technique fits this goal best?

  ARaising the context window limit so that longer replies fit without truncationBFine-tuning the model on many examples written in the desired voice and layoutCRetrieving brand guideline pages at query time and pasting them into the promptDCaching past replies and replaying the closest stored match to each new request

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  Retrieval Evaluation

  Your RAG answers are frequently wrong. Before tuning the prompt or model, you want to rule retrieval in or out as the culprit. Which single measurement most directly isolates the retriever's contribution?

  AWhether shrinking k lifts answer accuracy on those same failing casesBWhether the final generated answer matches the reference text for each failing caseCWhether the gold context sits within the top-k passages for each failing caseDWhether the model's confidence dips on each failing case versus a passing one

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  Agentic & Iterative RAG

  Compared with a standard pipeline that always prepends top-k chunks, what failure does on-demand retrieval gating specifically avoid?

  AInjecting off-topic passages into queries the model could answer from parametersBTruncating long passages that overflow the generator's strict context-window budgetCReranking passages whose dense vectors collide under aggressive index quantization noiseDCiting passages that were pulled from outside the grader's narrowly tuned corpus subset