LLM Evaluation Consulting

LLM evaluation consulting is the work of turning model quality into a release gate: a golden set of real cases, a written rubric, automated graders including calibrated judges, reconstructable traces, and a CI check that fails a bad prompt or model change. We build it in your cloud under a zero-retention, SOC 2-aligned process. You own the suite and the IP. A standard engagement is four weeks; financial-services reviews typically take 8–12 weeks.

Benchmarks like MMLU score a model on public tasks. Production evaluation scores your workflow on your tickets, documents, tools, and refusal cases. A model can win a leaderboard and still invent a policy, skip an escalation, or call the wrong tool. The consulting deliverable is a suite that fails those cases before customers see them, not a slide comparing frontier models.

A golden set is a versioned collection of real inputs with verified outputs or graded traces, including cases that must fail. Quality beats volume: 100–300 stratified cases catch most regressions if they cover intents, difficulty, empty retrieval, and adversarial inputs. Harvest from production traces, not imagination. Every incident should add a frozen case so the suite compounds.

A judge is trustworthy when the rubric is specific binary criteria, when it is calibrated against human labels on a held-out slice, and when the judge model and prompt are version-pinned. It is not trustworthy as the only score, on a 1–10 scale, or when it is the same model family as the system under test with no check. Ambiguous cases stay with a human reviewer.

Any change to prompts, models, retrieval, or tools runs the golden set in CI and posts a scorecard: overall pass rate, per-stratum deltas, cost and latency, and newly failing cases. Hard failures — schema, safety, groundedness — block merge. Soft drops need explicit sign-off. That is how LLM evaluation consulting stops silent regressions without freezing the team.

Work runs in the client cloud or VPC. We do not train on your data, we do not retain prompts after the engagement, and we do not share datasets across clients. You own the golden set, rubrics, judge configs, traces, and IP. The process is SOC 2-aligned. If a later vendor needs the suite, they inherit files in your repo, not a rented dashboard.

Four weeks is the standard path: discovery and failure inventory, harness and first golden slice, shadow scoring, then CI gate and handover. Financial-services programs with model-risk review typically take 8–12 weeks because the evidence package is co-developed with the second line. Subsequent products reuse the same gate pattern and ship faster.

No. Offline suites test what you anticipated; sampled live traffic tests what you did not. Online graders catch corpus drift, silent provider updates, and new user behaviors. Low-scoring traces feed a triage queue and become new golden cases. Both layers belong in a production program. Skipping either is how a green CI suite still surprises customers.