FROM BASIC RAG TO AGENTIC AND GRAPHRAG: A PRODUCTION BLUEPRINT
A practical series shows how to evolve basic RAG into agentic, adaptive, and graph-backed systems that cut cost and raise answer quality for real production use...
A practical series shows how to evolve basic RAG into agentic, adaptive, and graph-backed systems that cut cost and raise answer quality for real production use.
Poor retrieval increases cost and boosts confident hallucinations, so teams need measurable, modular RAG pipelines.
Agentic, adaptive, and graph-backed retrieval unlock harder tasks while keeping simple queries fast and cheap.
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terminal
A/B basic vs agentic/adaptive/graph flows on real workloads with guardrails for answer quality, latency, and cost per query.
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terminal
Retrieval evaluation harness with labeled and synthetic queries to catch confident hallucinations before release.
Legacy codebase integration strategies...
- 01.
Instrument current RAG for retrieval/answer quality and add an adaptive router in front; ship agent loops behind feature flags.
- 02.
For graph adoption, ETL entities/relations from existing stores and dual-run GraphRAG with vector search until parity.
Fresh architecture paradigms...
- 01.
Design a modular pipeline (classify → retrieve → evaluate → generate) and pick stores that support both vectors and graphs.
- 02.
Define per-query SLOs and route simple lookups to cheap paths while reserving agentic flows for complex tasks.
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