Practical guides, deep dives, and lessons from teams running LLM evaluation in production.
Most teams build, deploy, and hope. Here's why skipping LLM evaluation is a debt that catches up fast — and what a working evaluation setup actually looks like.
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Models hallucinate confidently. The question is whether you catch it before users do — and which detection approach fits your system architecture.
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You don't need a perfect dataset to start. Here's a practical structure for building an LLM test suite from zero, including what to include first and how to grow it.
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Red teaming isn't just for safety researchers. Here's how product teams run adversarial tests to find the edge cases standard evaluation never reaches.
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Adding LLM evaluation to CI/CD isn't as complex as it sounds. Here's the exact setup that works, what metrics to gate on, and how to handle evaluation latency.
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G-Eval changed how teams measure LLM output quality. Here's how it works, where it breaks down, and what task-specific metrics complete the picture.
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RAG systems fail at retrieval or at generation. Most teams only measure one. Here's how to evaluate both layers and know which one is causing your quality issues.
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Shipping without evaluation isn't a calculated risk — it's an unknown one. Here's what it actually costs when untested AI fails in production.
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What does an AI feature need to pass before it ships? A concrete checklist of quality gates from teams that maintain high reliability standards in production.
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Software regressions are binary. AI regressions are gradual and probabilistic. Understanding this difference is the prerequisite to building evaluation that actually works.
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Confident AI