Last updated: August 2026
RAG roles need proof you can chunk documents, embed, retrieve, and reduce hallucinations — show end-to-end pipelines, not isolated API calls.
Document stack: chunking strategy, embedding model, vector DB (FAISS, Chroma, Pinecone), reranker, and eval. Metrics: faithfulness, answer relevance, latency, and cost per query. Mention guardrails and citation in answers.
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Build FreeList what you built with — FAISS/Chroma for projects is fine; mention Pinecone if you have cloud trial experience.
Cite accuracy/faithfulness scores on a fixed test set or human eval sample size.
Use RAG engineer when JD mentions retrieval, knowledge base, or enterprise Q&A — mirror their language.
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