Evals are the product.
TL;DR
If you cannot measure whether an AI feature is good, you do not have a product — you have a demo. Brandlabs scopes eval suites (functional, qualitative, safety) as deliverables in week 1–3 so every later commit has a pass/fail bar.
Why do AI products need an eval suite before they need more features?
Features without evals optimize for looking good in a stakeholder demo. An eval suite defines what “good” means across functional correctness, qualitative quality, and safety — then CI enforces it. Teams that skip this invent success criteria after launch, when trust is already spent.
Most AI roadmaps still read like a feature list: chat, RAG, agents, dashboard. The missing artifact is the evaluation suite — a versioned set of cases, rubrics, and thresholds that say whether the system is allowed to ship.
At Brandlabs we put eval targets in the Discovery Sprint and Foundation phase (weeks 1–3). That is not process cosplay. It is how we keep a 12–20 week timeline honest when models, prompts, and data all move underneath the product.
What belongs in a production-grade eval suite?
Cover three dimensions: functional (did it do the task?), qualitative (is the answer clear, on-brand, useful?), and safety (does it refuse, escalate, and avoid harmful or non-compliant outputs?). Each dimension needs fixtures, scoring, and a deploy gate — not a spreadsheet reviewed once a quarter.
Functional evals catch broken tools, empty retrieval, wrong routing, and schema failures.
Qualitative evals catch tone, completeness, and “technically correct but useless” answers — the failure mode buyers feel first.
Safety evals catch medical overreach, PII leakage, jailbreaks, and silent confidence on empty evidence. For healthcare and regulated buyers, this dimension is non-negotiable.
We wire failing evals to block deploys the same way failing unit tests do. If that feels expensive, compare it to a single incident that forces a rewrite of trust copy, vendor posture, and sales cycle length.
How do evals change scope negotiation with an embedded team?
When evals are a contractual deliverable, “done” stops meaning “we shipped a UI.” Change requests become debates about thresholds and fixtures, not vibes. Buyers get evidence every Friday; sellers cannot hide behind demo scripts.
Scoping an AI engagement around screenshots invites infinite revision. Scoping around eval targets forces clarity:
- Which behaviors must never regress?
- Which languages and locales are in scope?
- What is the escalation path when the model is unsure?
That is why we say evals are the product. The chat UI is a surface. The suite is the definition of the system you actually sold.
Frequently asked questions
Can we use an LLM-as-judge for all scoring?
Sometimes for qualitative dims — never as the only safety gate. Calibrate judges against human labels, and keep hard rules (PII patterns, required refusals) as deterministic checks.
How large should the first suite be?
Start small enough to run on every commit: dozens of high-signal cases beat thousands of unmaintained ones. Grow from production failures and postmortems.
Do evals replace user research?
No. Evals prevent regressions and encode known failure modes. Alpha users still teach you which “good” answers are actually valuable.