#observability
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Prompt Injection Detection in Production: What to Alert On
Where injection detection sits in the serving path, which metric to tune (recall at a fixed false-positive rate), and how to shadow-test it.
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LLMOps Tools on GitHub: The Open-Source Stack
A layer-by-layer map of the open-source LLMOps stack on GitHub, from serving and gateways to tracing, evaluation and guardrails, plus how to vet a repo.
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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.
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LLMOps Best Practices: From Prototype to Production
LLMOps best practices that separate fragile demos from reliable production systems: prompt versioning, observability, evaluation and cost control.
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Token-Cost Observability: What You Measure vs What You Should
Most LLM apps track total spend and call it done. Per-feature cost, per-user attribution and anomaly bands all require deliberate instrumentation.
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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.