Golden datasets, LLM-as-judge scoring, and safety runners — hallucination, injection, toxicity, PII — wired into your CI. A prompt change that makes things worse stops at the gate, not in production.
▸ the 45-second version — in my own voice
50–200 real inputs with agreed-good outputs, versioned next to the code; LLM-as-judge scoring for faithfulness, relevance, and safety.
Hallucination, jailbreak, prompt-injection, toxicity, PII-leak, refusal, bias, consistency — plus cost and latency budgets.
The suite runs on every PR. A score below the ratcheted floor blocks the merge — with a scorecard your PM can read.
Your team extends the golden set and owns the gate without me.
fixed scope · quoted after a week-1 risk map · your repo, your CI
I map where your LLM feature can fail, score your current coverage against the failure modes that matter, and hand you a prioritized plan you own. The fastest way to a concrete quote.
Start here — get a quote →30–50 golden traces, one judge, one CI step that can block a merge. The regression you currently can't see, caught this month.
Get a quote →Safety runners, ratcheting floors, RAG retrieval metrics, cost budgets, runbook and handoff — the complete quality system, owned by your team.
Get a quote →fixed-price starting points from $497, plus a custom quote for larger or unusual builds — scoped in writing before we start, so you pay for your problem, not a package · every engagement ends with evidence you keep — and if the scoping shows I can’t help, I’ll say so and it costs nothing
A support-bot team couldn't tell whether a new prompt helped or hurt — every version "looked fine" in a spot check. We built 120 graded cases, added a faithfulness + citation gate, and wired it into CI.
The next "harmless" prompt tweak shipped — and quietly dropped citation coverage to 71%. Nobody knew until a customer hit it.
The same change turned the PR red in CI — caught in review, fixed before merge, never reached production.
Illustrative of the pattern — the exact dataset size and thresholds are scoped with you, not a specific client engagement.
Promptfoo and DeepEval where they fit, custom runners where they don't. The tool matters less than the discipline: computed scores from real commands, never opinions.
It adds retrieval-quality evals (context precision/recall, citation coverage) in front of the generation evals. I've shipped RAG systems where 100% of answers carry citations by design.
A minimal gate — 30 golden traces and one faithfulness judge in CI — typically lands in the first two weeks. The battery grows from there.
Common needs have a fixed price — a $497 audit, packaged builds from $4,997, care from $299/mo — and larger builds are scoped and quoted in writing after a short call, fixed scope rather than open-ended hours. Most engagements start with a one-week audit, and that fee credits into the build if you continue.
No. I extend and harden what you have before considering a rebuild. Evals sit alongside your existing tests; keeping your coverage and making it trustworthy is faster and cheaper than starting over.
Everything: the eval suite in your repo, the CI gate in your pipeline, a runbook your team operates, and no retained dependency on me. It runs on your models and your accounts.
You leave with a concrete plan either way — the call is free and the plan is yours.