Prompt evaluation.

Where Prompt evaluation fits on a long engagement.

Without evaluation, prompt work is guesswork with confidence. A change that improves one example often degrades three others, and nobody notices because nobody checked. A fixed test set with expected behaviour turns that from opinion into measurement.

Model-based judging scales but has its own biases: judges prefer longer answers, favour output from the same model family, and are inconsistent on borderline cases. Calibrating a judge against human scores on a sample is what makes its verdicts usable rather than merely available.

What an assigned team does with Prompt evaluation.

The test set is the asset, and it should grow from production failures. Every case where the system got something wrong is a case that belongs in the suite, so the same failure cannot recur unnoticed.

Maintaining that set continuously is ongoing work rather than a setup task, held within an agreed committed monthly capacity.

What we use Prompt evaluation for.

• Changes measured rather than assumed A fixed set scored before and after, so an improvement is evidenced.
• Regressions caught before release Evaluation in the pipeline, so a prompt change cannot quietly degrade other cases.
• A suite grown from real failures Production errors added as cases, so the same mistake cannot recur silently.

How Prompt evaluation capacity is assigned.

Evaluation capacity is assigned as explicit scope under ai engineering services, because an unmeasured system cannot be improved deliberately.