What you will learn
- How to design suites that measure what buyers care about
- Where automated checks end and human review begins
- The build order from one suite to a full evaluation service
- Why a leaderboard without methodology is marketing, not measurement
What this project is
Teams ship models on vibes when evaluation is hard: a few impressive demos, a benchmark number borrowed from a paper, a colleague's approval. An evaluation platform replaces vibes with repeatable suites, versioned test data, and results that can be compared across models and over time.
Good evaluation is specific. A support model needs accuracy on real tickets, tone checks, and refusal behaviour; a code model needs tests that run, not text that looks right. Generic scores hide the failures that matter to a particular buyer, so suites must be built around real tasks.
Core features
- Versioned test suites with tasks, prompts, scoring rules, and expected outputs.
- Automated scorers for accuracy, format, latency, and cost per task.
- Human review queues with rubrics, agreement scoring, and sampling of automated passes.
- Leaderboards with stated methodology, test versions, and per-task breakdowns.
- Regression tracking comparing each model version against baselines and previous releases.
- Report exports that buyers and auditors can read without platform access.
How it is built
- One suite first. A single task family, automated scoring, and versioned test data, proven on real models.
- Comparison second. Baselines, regression views, and per-task breakdowns that make differences visible.
- Human review third. Rubrics, sampling, and agreement scoring for qualities automation misses.
- Leaderboards fourth, once methodology and test governance are solid enough to publish.
- Customer suites last. Private test sets and custom tasks for buyers with their own data.
Technology considerations
Test data governance is the core discipline: test sets must stay separate from training data, and access must be limited so results cannot be gamed. Scoring should run in isolated jobs so a model cannot read the test set or interfere with scoring. Results storage needs to keep full per-task detail, since aggregate scores hide regressions in slices that matter. Build cost depends on human review volume; confirm with a quote.
- Versioned test suites with access controls and usage logs.
- Isolated scoring jobs that keep test data away from models.
- A results store with per-task detail for every run.
- Human review tooling with rubrics and agreement measurement.
Challenges to plan for
- Test leakage. If test data reaches training, scores become meaningless, so separation must be enforced technically, not by policy alone.
- Benchmark gaming. Teams optimise for the published suite, so suites need refresh cycles and private held-out sets.
- Human subjectivity. Reviewers disagree on quality, so rubrics and agreement scoring matter more than reviewer count.
- Cost of thoroughness. Large human-reviewed suites are expensive, so sampling and automation must be balanced deliberately.
A worked example
A company choosing between three vendor models for support ticket classification needs evidence, not sales decks. It builds a test suite from five hundred real, anonymised tickets with known categories, plus edge cases: tickets in two languages, tickets with attachments mentioned, and tickets where the customer changes topic mid-thread. Each vendor model runs against the same suite, in isolated scoring jobs, and results show per-category accuracy, latency, and cost per ticket, not a single headline score.
The cheapest model wins on overall accuracy but fails badly on multi-topic tickets, which are the company's most expensive support cases. The most expensive model handles them well but costs three times as much per ticket. The company picks the middle model, documents the decision with the full per-task breakdown, and re-runs the suite quarterly as vendors update their models. When a vendor later claims a major improvement, the company asks for the suite results rather than accepting the claim, and the evaluation platform is what makes that conversation possible.
Who it is for, and its limits
This fits model vendors, marketplaces, and teams whose model choices carry real cost, such as customer-facing AI products or regulated work.
It does not fit a team shipping one internal model where a small held-out set and manual review suffice. It also does not certify safety on its own; evaluation supports a safety case, it does not replace one.
Frequently asked questions
What makes a benchmark trustworthy?
Stated tasks, versioned test data, scoring rules, and separation from training data, plus per-task results rather than a single aggregate number.
How much human review is needed?
Enough to cover what automation misses, sampled across task types and model versions, with agreement scoring to check reviewer consistency.
Should leaderboards be public?
Only with published methodology and test versions. A leaderboard without those is a ranking claim buyers cannot verify.
How often should suites be refreshed?
When models start optimising for the suite, or when the task changes. Private held-out sets extend a suite's useful life.
Can evaluation use customer data?
Yes with consent, access controls, and anonymisation where needed, and customer suites should stay private to that customer.
Why do per-task results matter more than an overall score?
Because an overall score hides failures in the slices that matter most. A model can lead on average while failing badly on the cases that cost the most to get wrong.