Operating LLMs in production — eval, observability, cost, latency.
Production engineering for LLM systems. Evaluation pipelines, online observability, cost and latency tradeoffs, prompt-version drift, A/B on real traffic, and the cases where the LLM-stack hype crashes into the operational reality.
Best Vector Database for RAG: A Practical Comparison (2026)
Pinecone, Weaviate, Qdrant, pgvector, Chroma, Milvus — benchmarked on recall@k, p99 latency, filtered search, and cost at real production scale.
Lead investigation
Semantic Caching for LLM Serving: When the Cache Hit Is Not a String Match
Exact-match caching misses most LLM cache hits — paraphrases tank hit rate. Semantic caching, threshold tuning, and the production failure modes that bite.
LLM Eval Pipelines in CI/CD: Gates That Actually Catch Things
Running LLM evals in CI is easy to set up and easy to get wrong. How to build quality gates and red-team gates that block bad prompts before they ship — and why a passing CI eval is not the same as a working production system.
Prompt Versioning and Deployment: The Operational Workflow
Versioning prompts is the easy part. The operational hard parts — decoupling prompt releases from code deploys, labels for staging vs production, rollback, and not blowing your latency budget — are where teams actually get stuck.
Archive
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RAG Observability: Monitoring the Retrieval Layer in Production
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Self-Hosted vs API LLMs: The Operational Tradeoffs
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Guardrails in the Serving Path: Defense in Depth for LLMs
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LLMOps Best Practices 2024: From Prototype to Production-Grade
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Model Registry Patterns That Actually Work
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Token-Cost Observability: What You Measure vs What You Should
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Training/Serving Skew: The Silent Killer
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What this site is for
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