▸ Tag · #rag
Posts tagged #rag.
8 posts with this tag.
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AI7 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.
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ArchitectureCaching 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.
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ArchitectureCircuit 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.
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ArchitectureMessage queues: handling the heavy lifting of document processing
Stop running embeddings inside the request-response cycle. A production-grade document ingestion pipeline — staged workers, exponential backoff, dead-letter quarantines, batched embeddings, and queue-depth autoscaling that keeps your AI app from melting under a 500-page PDF.
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ArchitectureRate limiting: protecting your AI wallet
One runaway agent loop = $5,000 OpenAI bill. Why request-per-second limits lie for LLM apps, how to architect hierarchical token-bucket limits across global / tenant / user layers, and adaptive throttling patterns that protect margins without breaking UX.
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ArchitectureAPI Gateway: the front door of your AI stack
Stop exposing LLM providers directly to the frontend. The gateway pattern for AI apps — JWT-scoped tenant isolation, model aliases, denial-of-wallet rate limiting, streaming-safe timeouts, and the wallet-saving guardrails every senior engineer needs.
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AIWhy 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.
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AIPicking 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.
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