Glossary · Definition
LLM evals
LLM evals are automated tests for AI behaviour: a set of realistic inputs with expected results, scored on every change to prompts, models or data.
By Nythrex EngineeringUpdated 1 min read
In plain words
Just as unit tests protect code from regressions, evals protect AI features. They tell you whether a new prompt, model version or retrieval tweak made things better or worse — across all your important cases, not just the ones you happened to try.
When it matters to you
- Before choosing a model
- On every prompt or model change
- When a provider releases a new version
Common pitfalls
- Only synthetic, easy test cases
- An LLM judge nobody calibrated
- Reporting quality without cost and latency
Keep reading
GuideLLM evals for non-ML teamsHow to evaluate an LLM feature without a data-science team: build a test set, choose metrics, use LLM-as-judge carefully, run evals in CI and read the results.GlossaryPrompt engineeringPrompt engineering is designing the instructions, examples and context given to a language model so it performs a task reliably.ServiceAI proof of conceptA time-boxed AI proof of concept on your real data: test set, thin end-to-end slice and a go / no-go backed by accuracy, latency and cost numbers.
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