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RAG in Production

Everything that breaks when retrieval-augmented generation meets real users — and how to fix it.

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  1. 01

    Why your RAG implementation is failing in production (and how to fix it)

    Vector-only retrieval is the silent killer of production RAG. Hybrid search with BM25, reciprocal rank fusion, smarter chunking, re-rankers, and an evaluation harness — the production checklist that turns a flaky demo into a reliable system.

    AI · 6 min read
  2. 02

    7 mistakes you're making with your production RAG stack (and how to fix them)

    Naive chunking, no reranker, embedding drift, latency blowups, vibe-checking — the seven structural mistakes that turn a slick RAG demo into a production nightmare, and the fixes that actually ship.

    AI · 7 min read
  3. 03

    Picking the right RAG stack: vector databases for AI engineering

    pgvector, Pinecone, Weaviate, Qdrant — a 2026 field guide. Which vector store to pick for your AI app, why hybrid search matters, and how to ship without painting yourself into a corner.

    AI · 7 min read
  4. 04

    Caching for speed: Redis and semantic layers in RAG

    Stop paying for the same LLM call twice. Two-tier caching — exact-match Redis keys plus semantic vector lookups via RedisVL — that cuts RAG latency from seconds to milliseconds and slashes API spend by up to 80%. With tenant isolation, TTL tiers, and the precision metrics that keep it honest.

    Architecture · 6 min read
  5. 05

    Circuit breakers: preventing cascading failures in your vector DB

    A slow vector DB kills SaaS faster than a dead one. The circuit-breaker pattern for AI infrastructure — closed/open/half-open states, fallback tiers, semantic caches, LLM-only mode, and Laravel-friendly wiring to keep production from melting under one bad dependency.

    Architecture · 7 min read