Evals (evaluation suite)

Evals are to AI systems what automated tests are to software. An evaluation suite pairs a golden dataset of representative inputs with expected outcomes and a scoring method: exact match for extraction, rubric-based judgement for free text, tool-call correctness for agents, and groundedness checks for retrieval. The suite runs on every change and produces a score you can compare across versions.

Scoring methods vary by task. Deterministic checks (did the JSON parse, did the amount match) are cheapest and most trustworthy. Model-graded checks use a second model with a rubric to judge tone, completeness or safety; they scale well but need their own calibration against human labels. Human review remains the reference for the hardest cases.

Evals are not a demo and not a vibe check. A demo shows the system working on inputs someone chose; evals show how often it works on inputs users actually send, including the ugly ones. They are also not one-time acceptance tests: the suite grows as production failures are added to it.

• Founder / CEO: the eval score is the honest answer to "does it work?", and the number you should ask for before approving launch.
• CTO / Head of Engineering: evals make model swaps, prompt edits and provider migrations safe, and let you say no to changes that lower quality.
• Product manager: turns "the AI feels worse this week" into a measurable, prioritisable regression.
• Compliance officer: a documented eval process with thresholds is what a model-risk review will ask to see.
• CFO: lets you trade quality against cost deliberately, by showing exactly what a cheaper model gives up.

Evals exist because language models fail quietly. Output stays fluent while accuracy drops, so nobody notices until customers complain or a regulator asks. Traditional unit tests cannot cover non-deterministic output, and manual review does not scale. An evaluation suite gives a repeatable score, catches regressions before release and documents quality over time. The trade-off is upfront effort: building a golden set and scoring rules takes real work, and model-graded scoring needs calibration. Eazyware's stance is evals over demos precisely because that effort is the difference between a pilot and a product.

• Scoring a KYC extraction pipeline field by field against checker-verified documents before each model update.
• Judging a support agent's replies for correctness, policy adherence and escalation behaviour on a thousand historical tickets.
• Measuring text-to-SQL accuracy against a golden question set maintained by the data team.
• Checking a clinical summarisation assistant never introduces medications absent from the source note.
• Testing a voice agent's booking flow on transcribed real calls, including interruptions and mixed-language turns.
• Comparing three candidate models on a retailer's product-tagging task to pick the cheapest one that clears the threshold.

Technique / practiceA practice your team adopts, backed by tooling you own. Every Eazyware build ships with an eval suite, from ProofRun onward, and LLM applications work treats the eval score as the acceptance criterion rather than a demo.

Eazyware service that covers it: LLM Application Development. Starting prices are on the pricing page.