What's the minimum viable LLM eval setup?

A frozen test set of 50–200 representative prompts with reference answers, an automated scorer (exact match for structured outputs, LLM-as-judge for free-form), and a CI gate that fails the build if pass rate regresses. That's enough to catch the majority of production regressions before they ship.

Can you trust an LLM to grade another LLM?

For most subjective tasks, yes, with caveats. LLM judges should be calibrated against human-labelled ground truth on a hold-out set, run with deterministic settings (low temperature, structured output), and ideally run multiple times with majority voting on important decisions.

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LLM evaluation

How teams measure whether an LLM application is actually working in production, without relying on vibes.

LLM evaluation is the engineering discipline that lets teams ship AI features with the same confidence they ship anything else. It covers offline benchmarks, online A/B testing, regression suites, and the human-in-the-loop scoring that calibrates the automated checks.

The state of the art shifted in 2025: LLM-as-judge methods became reliable enough for production gating when paired with deterministic structural checks. Notifire tracks the eval-framework releases, public benchmark drama, and the tools (Ragas, Braintrust, Inspect, OpenAI Evals) that engineering teams actually adopt.

LLM evaluation

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Frequently asked questions

What's the minimum viable LLM eval setup?

Can you trust an LLM to grade another LLM?

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