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▸ Blog series

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

    Vector-only retrieval quietly breaks production RAG. Hybrid search, BM25, rank fusion, re-rankers and evals: the fixes that make it reliable.

    AI · 7 min read
  2. 02

    7 mistakes wrecking your production RAG stack

    Naive chunking, no reranker, embedding drift, latency blowups: the structural mistakes that wreck a production RAG stack, and the fixes that ship.

    AI · 9 min read
  3. 03

    Picking the right RAG stack: vector databases for AI

    pgvector, Pinecone, Weaviate, Qdrant: a 2026 field guide to picking the right vector store for your AI app, with hybrid search and scaling tips.

    AI · 8 min read
  4. 04

    RAG architectures: traditional, agentic, corrective

    Compare traditional, agentic, and corrective RAG architectures, with the latency, cost, and accuracy trade-offs that decide which fits your AI app.

    AI · 8 min read
  5. 05

    Caching for speed: Redis and semantic layers in RAG

    Stop paying for the same LLM call twice. Two-tier caching with Redis keys and RedisVL semantic lookups slashes RAG latency and trims your LLM API bill.

    Architecture · 6 min read
  6. 06

    Circuit breakers: stopping vector DB failures

    A slow vector DB kills SaaS faster than a dead one. The circuit-breaker pattern for AI infra: states, fallback tiers, and Laravel-friendly wiring.

    Architecture · 9 min read