Most LLM features ship on vibes. A product manager tries the new prompt, agrees it "feels better," and the team merges. Six weeks later a customer files a ticket about a regression and the team rolls back, with no idea what they rolled back to.
The fix is not better prompts. The fix is treating the evaluation harness as a first-class product surface.
What the harness must do
A useful eval harness has four properties.
It is reproducible. Same inputs, same model version, same scaffolding produces the same numeric output. That means deterministic settings where possible and bootstrapped confidence intervals where not.
It is task-grounded. Generic benchmarks (MMLU, HellaSwag) tell you almost nothing about whether your feature works. The metric you care about is task-specific: pass rate on the customer's actual queries, hallucination rate on the documents you actually retrieve, refusal rate on the prompts you actually receive.
It supports human review. Some judgments cannot be automated. A reviewer queue, structured rubrics, and inter-rater reliability tracking belong in the harness, not in a spreadsheet.
It runs on every change. Not just before release. Every prompt edit, every retrieval-index rebuild, every model upgrade.
What changes when you have one
Two things change. First, your team starts arguing about metrics instead of opinions, and the arguments converge faster. Second, you start catching regressions before customers do — which is the whole point.
The cost is real: a few weeks of engineering up front, plus ongoing review effort. The cost of not having one is denominated in customer trust, which is not a currency you can refund.
Working with us: Rizmi Labs builds evaluation harnesses for product teams who need their LLM features to actually work. Get in touch to discuss.