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A programme team rolled out an internal assistant to help staff answer policy questions. The demo was flawless. Two weeks later, after a model update, the assistant started answering with more confidence and fewer caveats. Nobody noticed until a high-stakes query landed on the wrong desk.
This is the core production problem with LLMs: the output feels plausible until it doesn't. If your quality control is "does this look right?", you will ship regressions. Evaluations ("evals") are how you avoid that.
must‑pass scenarios
cases from real usage
critical safety failures
An evaluation (eval) is a repeatable test suite that measures your AI system against clear criteria—accuracy, grounding, safety, tone, format, latency—and catches regressions before users do.
Think of it like unit tests and CI for LLM features: you're not trying to prove perfection. You're trying to make failures detectable, measurable, and fixable.
In early prototypes, teams can eyeball outputs. In production, that breaks for four reasons:
The fastest way to build trust is to split tests into two buckets: things that should always be true, and things that should be true most of the time.
| Deterministic checks (unit tests) | Probabilistic checks (eval metrics) |
|---|---|
| JSON/schema validity | Tone, clarity, concision |
| PII redaction present | Helpfulness for ambiguous prompts |
| Citations included when required | Factual correctness under long-tail inputs |
| Forbidden content never appears | Consistency across paraphrases |
| Latency budget respected | Grounding quality (faithfulness) |
LLM-as-a-judge means you use a model to score outputs against a rubric (e.g. “Does this answer cite policy?”, “Is it safe?”, “Is it in the required tone?”). It scales review, but it must be treated like any other measurement system: calibrated, monitored, and occasionally audited by humans.
Define a rubric with 4–6 dimensions (Accuracy, Grounding, Safety, Tone, Format, Latency).
If your rubric is vague, the judge will be vague. Make every criterion observable.
Start with a small human-labelled golden set to calibrate the judge.
If the judge doesn’t correlate with humans on goldens, it will not save you in production.
Use thresholds and confidence bands (e.g. must be ≥ 90% on goldens).
Single scores hide failures. Track by dimension and scenario class.
Log everything: inputs, retrieved context, model version, prompt version, judge scores.
Without an audit trail you cannot debug incidents or defend decisions.
| Option | When to Use | Trade-offs |
|---|---|---|
| Human review only | Very low volume, early prototypes, high-stakes edge cases | Doesn’t scale; expensive; inconsistent; slow feedback loops |
| LLM-as-a-judge | Medium/high volume, clear rubrics, you need regression testing | Requires calibration; can be biased; must be audited periodically |
| Hybrid (recommended) | Most production systems: judge for scale + humans for goldens and audits | Requires an operating cadence and ownership across teams |
Days 0–30: define quality dimensions + build golden set + instrument logging.
Skipping logging means you’ll debate opinions instead of diagnosing failures.
Days 31–60: build regression suite from real usage + add release gates in CI.
If gating is optional, it will be skipped under delivery pressure.
Days 61–90: add LLM-as-a-judge scoring + monthly human audit + exec dashboard.
If metrics aren’t reviewed, the system will drift until it breaks.
If you want reliable AI in production, you need an evaluation system that makes regressions visible. Evals don't slow you down—they stop you shipping the same problem repeatedly.
For leadership teams, evals turn AI from a demo into an operational capability: measurable, governable, and continuously improving.
Shipping LLM features without eval gates is like deploying code without tests. We build evaluation suites, monitoring, and governance as part of end-to-end delivery.
Learn about our method →