When to use Contextual Retrieval (and when not to) in RAG
Honest trade-offs for Contextual Retrieval in RAG: when it beats long-context, hybrid search, or agentic search with Claude, and how to decide fast.
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
Honest trade-offs for Contextual Retrieval in RAG: when it beats long-context, hybrid search, or agentic search with Claude, and how to decide fast.
Guardrails leaders need before scaling Contextual Retrieval: retrieval-time access control, provenance, safe failure modes, and audit logging with Claude.
The habits, ownership models, and review norms that make Contextual Retrieval stick on real engineering teams building agentic RAG with Claude.
A whole-loop cost model for Contextual Retrieval in agentic RAG with Claude: where savings come from, what to instrument, and how fast it pays back.
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.