Evals
Treat evaluation as a first-class deliverable. Regression suites, safety dims, and shipping without demo-grade guesswork.
ICP job: Stop shipping LLM features that only look good in demos
How to evaluate refusal, escalation, medical-advice boundaries, and hallucinated dosages — before a health assistant meets real users.
Golden sets, adversarial sets, and ownership so your LLM suite still means something after six months of prompt thrash.
The Brandlabs Discovery Sprint pattern for setting LLM eval targets in weeks 1–3 — before feature thrash eats the timeline.
Sampling, cost caps, flake handling, and which LLM eval suites must stay required vs nightly — so production cannot silently regress.
Why we treat the eval suite as a first-class deliverable — and how it changes how we negotiate scope.
The cultural and technical loop that turns production incidents into permanent guardrails.
Planned and live topics for this pillar. Priority 1 ships first.
Query: Run LLM evals in CI/CD
Sampling, cost caps, flake handling, and which suite is required vs nightly.
Query: Why LLM evaluation suites matter for production AI
Eval suite as a scoped deliverable that changes how you negotiate engagements.
Query: Turn production AI incidents into eval regressions
Cultural + technical loop from incident → fixture → CI gate.
Query: How to evaluate safety of healthcare chatbots
Refusal, escalation, medical advice boundaries, hallucination of dosages/claims.
Query: LLM evaluation dimensions for product teams
Brandlabs Discovery Sprint pattern — set targets in week 1–3.
Query: How to build LLM regression test datasets
Golden sets, adversarial sets, and who owns updates.
Evaluating multilingual health assistants (India)
Query: Evaluate Hindi Hinglish healthcare chatbot quality
Locale fixtures, code-switching, and safety across languages — Myna lessons.
Groundedness evals for RAG in regulated domains
Query: How to measure RAG groundedness healthcare
Citation fidelity, refusal when retrieval is empty, and poison documents.
LLM-as-judge: when it helps and when it lies
Query: LLM as judge evaluation reliability
Calibration, bias, and when human rubrics still win — Precision lessons.
Offline vs online evals for AI products
Query: Offline vs online LLM evaluation
When batch suites lie; when production scoring is too slow.
Closing the loop from Learn content to eval agents
Query: AI evaluation platform for product teams
How Precision’s Learn hub and Agent Library reinforce shipping quality — lab proof.
Eval cost budgets that don't bankrupt the sprint
Query: Cost of running LLM evaluation suites
Sampling strategies and model tiers for judges vs product models.
Canonical hub: https://brandlabs.app/blog/topics/evals-production-quality