Automated Evaluations / Evals
Our evaluation layer tests model and system behavior against representative production workloads rather than relying solely on generic benchmarks or manual spot checks.
What evals measure
Evals can measure classification quality, extraction accuracy, reasoning, retrieval quality, tool use, structured outputs, refusal behavior, and citation quality. The evaluation design depends on the task.
Evaluation unit
A useful eval defines:
- Input
- Expected behavior
- Scoring method
- Acceptable threshold
- Failure classification
For exact extraction, scoring can be deterministic against ground truth. Retrieval can be evaluated against expected evidence. Tool-use tests can verify both tool selection and argument structure. More ambiguous reasoning may require rubric-based review or independent model or human judgment.
A single scoring method is not appropriate for every workload.
Production relevance
We prefer evaluation cases drawn from the conditions the system is expected to encounter. That includes routine examples, difficult examples, edge cases, previously observed failures, and cases where the economic consequence of error is high.
An evaluation suite that contains only easy examples can create false confidence while failing to test the operating boundary that matters.
Continuous use
Evals are not only for initial model selection. Changes to models, prompts, retrieval, tools, workflows, and context construction can all alter behavior. The same evaluation suite can therefore participate in deployment gates and regression testing.
- Yes → continue
- No → block / investigate
Limits
An eval measures what it was designed to measure. Passing a suite is not proof of universal correctness, and a stale suite can stop representing production as the company changes.
We treat evaluations as versioned engineering assets tied to real workloads. Their purpose is not to produce an impressive score. It is to give us repeatable evidence that a system change is sufficiently reliable for the work we intend it to perform.
Evaluators can differ by failure class
A single evaluator does not need to judge every property of an output. One test can validate schema. Another can check retrieval evidence. Another can score task correctness. A separate reviewer can inspect whether the answer contains unsupported claims.
This decomposition makes failures more actionable. If a candidate fails because the correct evidence was never retrieved, changing the model may be the wrong intervention. If retrieval is correct but the model misinterprets the evidence, the failure belongs elsewhere.
For important workloads, evaluation results can therefore preserve both a final pass/fail state and the component-level reason behind it. That allows the evaluation layer to answer not only whether the system got worse, but which part of the system produced the regression.