AI evaluation & assurance
Know what changed before your AI goes live.
Test unsupported answers, prompt injection, data leakage and tool misuse before approving a release.
An implementation example
A better demo is not a release criterion
Build a versioned evaluation set from real tasks, difficult cases and expected refusals. Compare changes to prompts, models and retrieval against the same evidence.
A release decision with evidence
An Australian business needs evidence that a proposed assistant improves a specific operational task.
A failure to account for
The evaluation set contains the same examples used to design the prompt.
Illustrative scenario, not a customer case study.
Prepare the conversation
What needs attention in your system?
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AI, data & automation
Look beyond the average score.
Representative tasks
Build an evaluation set that includes ordinary requests, difficult cases and expected refusals.
Regression comparisons
Compare model, prompt and retrieval changes against the same versioned inputs and acceptance criteria.
Operational evidence
Review traces, cost, latency and failure behaviour alongside answer quality before approving a release.
Tool behaviour
Exercise denied permissions, malformed inputs and interrupted actions as part of the evaluation.
Adversarial inputs
Test hostile document instructions and requests that try to cross the application’s trust boundaries.
Release comparison
Retain model, prompt, source and configuration versions so an unexpected result can be investigated.
Release confidence comes from the difficult cases
The fragile approach
Judge a handful of good answers
Selected examples miss harmful regressions, missing citations and cost changes under realistic traffic.
The intended approach
Review an explicit release record
Keep task results, failures, latency and cost together. Record who accepted the remaining limitations and the rollback trigger.
Usually not. Separate task quality, access control, action safety, response time and cost. A critical permission failure must not disappear inside an average score.
Read the engineering behind it
Discuss ai evaluation & assurance
Bring the workflow, the constraints and the questions your team needs to resolve.