All posts
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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.
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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.
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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.
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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.
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RAG Observability: Monitoring the Retrieval Layer in Production
When a RAG system gives a bad answer, the retrieval layer is usually to blame — and your LLM monitoring can't see it. How to instrument retrieval quality with context precision, recall, and faithfulness in production.
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Self-Hosted vs API LLMs: The Operational Tradeoffs
The self-host-versus-API decision is usually framed as a cost-per-token comparison. The real tradeoffs are operational — GPU memory math, who owns reliability, and the hidden engineering cost that the token spreadsheet ignores.
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Guardrails in the Serving Path: Defense in Depth for LLMs
Guardrails are not a single check you bolt on — they're layers in the request path, each catching what the others miss. How to place input, output, and behavioral guardrails without wrecking latency.
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LLMOps Best Practices 2024: From Prototype to Production-Grade
A practitioner's guide to the LLMOps best practices that separate fragile demos from reliable production systems: prompt versioning, observability, evaluation, and cost governance.
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Model Registry Patterns That Actually Work
What the hype skips about model registries, what mature teams actually do, and how to avoid the metadata graveyard most registries become.
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Token-Cost Observability: What You Measure vs What You Should
Most LLM apps track total spend and call it done. The interesting signals — per-feature cost, per-user attribution, anomaly bands — require deliberate instrumentation.
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Training/Serving Skew: The Silent Killer
How training/serving skew happens, why it's so hard to see, and the specific places to look when your model works in eval and breaks in prod.
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What this site is for
LLMOps Report covers ML observability and MLOps from a production-engineering perspective. Here's what we publish.
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MLOps Tool Review: Arize vs Evidently
An honest comparison of two ML observability tools—where each fits, where each frustrates, and what neither one solves.
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Concept Drift Detection in Production: Practical Thresholds
How to actually detect concept drift in live systems, what thresholds matter, and why your monitoring dashboard is probably lying to you.