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Practice 04

AI Model Evaluation

AI model evaluation, red teaming and RLHF training support for organizations building and deploying language models.

An evaluator reviewing an AI model evaluation dashboard showing quality, accuracy, relevance and safety scores

The failures that matter most are the ones only a domain expert will catch.

What we usually hear

Signs your evaluation process has gaps

Our benchmarks look fine, but the model still fails in our domain.

The people testing the system are the people who built it.

We cannot show a risk committee how it behaves at the edges.

Two reviewers score the same output differently.

We fixed an issue but cannot prove it stayed fixed.

Our evaluators do not have real expertise in the field we operate in.

If two or three of these sound familiar, the work below is where we would start.

Key solutions

What we deliver

Model evaluation and research

Structured review against criteria agreed with your team, scored consistently across evaluators so results can be compared over time.

Red teaming

Deliberate adversarial testing to surface harmful, biased or unsafe behaviour before release rather than after it.

Training support

Preference data and human judgment for RLHF and related methods, supplied by evaluators with real expertise in the relevant field.

Framework

Findings return to evaluation so a fix can be verified.

Define criteria with your domain Evaluation structured review Red teaming adversarial testing Scoring traced findings Training feedback Findings re-enter evaluation so a fix is confirmed rather than assumed
Evaluation pipeline. The feedback path is what separates an audit from an improvement process.

Expected business outcomes

What you have at the end

Documented evaluation criteria agreed with your teamArtifact
Reproducible scoring across multiple evaluatorsMeasure
Findings traced to specific failure modesArtifact
Coverage across the domains that matter to youMeasure
Feedback in a form usable for trainingArtifact

Proof points

External expert review is established practice at the frontier.

We do not publish client evaluation results. What follows is the public practice this work is modelled on.

Published system cards

Major model developers, including Anthropic and OpenAI, publish model or system cards documenting evaluation and external red teaming carried out before a release.

External reviewers

Those documents describe the use of outside domain experts specifically because internal teams share the assumptions of the system they built.

Traceable findings

Results are recorded against named failure modes rather than as an overall pass, which is what allows a fix to be confirmed on re-test.

Discuss this practice.

Send a short brief describing the situation. We respond with an approach, the team who would deliver it, and the measures we report against.

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