Migrate Your RAG Workflow to Contextual Retrieval
A staged playbook to move an existing Claude RAG workflow onto contextual retrieval: shadow indexing, eval gates, canary rollout, and instant rollback.
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A staged playbook to move an existing Claude RAG workflow onto contextual retrieval: shadow indexing, eval gates, canary rollout, and instant rollback.
Build an eval loop for contextual-retrieval Claude agents: recall and precision at k, a calibrated LLM judge, and CI gates that block quality regressions.
Harden Claude RAG agents against prompt injection and data leaks with sandboxing, least privilege, server-side secrets, and approval gates.
Keep contextual-retrieval RAG cheap and fast on Claude: prompt caching, the Batch API, reranking, and context budgets — with a clear cost decision table.
Fix the top failure modes in contextual-retrieval Claude agents: retrieval loops, wrong tool calls, and hallucinated arguments — with traces and fixes.
What to put in a Claude agent's context and what to cut: ordering reranked chunks, deduping, citations, and grounding rules for better RAG answers.
Expose contextual retrieval via MCP: auth at the boundary, strict schemas, structured errors, and idempotent results so Claude agents search safely.
Reusable contextual RAG patterns: structure the enrichment prompt, shape retrieved chunks, and expose retrieval as a typed tool Claude agents call on demand.
Runnable walkthrough: chunk, contextualize with Claude Haiku + prompt caching, index vectors and BM25, fuse, rerank, and ground a Claude agent.
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