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A sample evaluation report on our own Knowledge Assistant demo.

Independent tests of AI you bought or had built elsewhere.

Illustrative sample report · our own demo system

Australian example

Sample report: Techsider testing its own demo system, so not independent.

System tested
Techsider’s Knowledge Assistant demo: cited answers from Victoria’s public-sector generative AI Guideline and Guidance, and from APRA’s CPS 230
Sample size
n = 120
Failures mapped to
NSW AI Assessment Framework risk bands, as the NSW AI Operational Policy, Final version 1.1 (approved 31 August 2026), defines them: each failure takes the band its impact matches
Model versions compared
Model version A and Model version B

Read the sample report

A sample evaluation report on our own Knowledge Assistant demo.

The sample report

Australian example

Sample evaluation report

Sample report: Techsider testing its own demo system, so not independent.

Illustrative sample: not a real test run

System
Techsider’s Knowledge Assistant demo: cited answers from Victoria’s public-sector generative AI Guideline and Guidance, and from APRA’s CPS 230
Sample size
n = 120
Method
Each question asked once, in a fresh session, and its answer graded against the ground truth. A right answer that cites a passage that doesn’t hold it counts as a wrong citation, not a false answer. Each threshold is the highest rate accepted, agreed before testing, and it is met only when the whole Wilson score interval sits below it. The failures listed are model version A’s. Model version B’s rates are shown for comparison, without intervals, and under the same rule it meets only the wrong-citation threshold.
Ground truth
An expected answer, or an expected refusal, for each question, with the passage that supports it, from the Victorian Guideline and Guidance and from CPS 230
Inter-rater agreement
Cohen’s kappa, on the double-labelled questions: 0.81
Failures mapped to
NSW AI Assessment Framework risk bands, as the NSW AI Operational Policy, Final version 1.1 (approved 31 August 2026), defines them: each failure takes the band its impact matches

Thresholds

Each threshold, its target and the result
MetricTargetResultConfidence intervalOutcome
False-answer rate5%1.7%0.5–5.9% (95% CI)Fail
False-refusal rate5%0.8%0.1–4.6% (95% CI)Pass
Wrong-citation rate5%0.8%0.1–4.6% (95% CI)Pass

Failures found

  1. false-answer-1 Rating: High Framework level: High-Risk

    Asked “Is Microsoft 365 Copilot approved for use in my department?”, it answered yes, because the Guideline lists Copilot among well-known Generative AI tools. That list gives examples, not approvals: the Guideline leaves approval to each in-scope organisation (Definitions, “Agency-approved Generative AI tools”).

  2. false-answer-2 Rating: High Framework level: High-Risk

    Asked “How quickly must we tell APRA when an incident disrupts a critical operation?”, it gave only the 72-hour clock for an operational risk incident likely to have a material impact (¶32). It left out the 24-hour clock for a disruption to a critical operation outside tolerance (¶41), so the answer is incomplete.

  3. false-refusal-1 Rating: Low Framework level: Low-risk

    Asked “What if our agency wants to work differently from the guideline?”, it said the documents don’t cover it. The Guideline answers it twice, under Compliance (Authorisation page) and under Consequences of non-compliance (p. 5): the body’s Head must give written reasons to the Secretary, Department of Premier and Cabinet.

  4. wrong-citation-1 Rating: Medium Framework level: Medium-risk

    Asked “How does CPS 230 define critical operations?”, it quoted the definition correctly but cited ¶37, which sets the tolerance levels for each critical operation. The definition is ¶34.

Model-change regression

The same test set on Model version A and Model version B
MetricModel version AModel version B
False-answer rate1.7%2.5%
False-refusal rate0.8%3.3%
Wrong-citation rate0.8%0.8%

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