Context Design for Retrieval-Grounded Claude Agents
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.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
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.
End-to-end contextual retrieval for Claude agents: chunk enrichment, dual embedding plus BM25 indexes, rank fusion, and reranking before the model reads.
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Building AI automation workflows using n8n and Claude API -- practical patterns for business process automation without a full development team.
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