Controlled Quick Check Benchmark
Evidence-grounded research vs. AI prediction
We tested 20 product scenarios across ideas, features, designs, copy, names, ads, packaging, and other common Quick Check use cases.
20Product scenarios
200Simulated interviews
45.53 / 50Askli Quick Check
23.45 / 50Best one-shot frontier baseline
Research usefulness against hidden evidence
Participant evidence changed the answer.
Askli Quick Check45.53 / 50
OpenAI one-shot23.45 / 50
Gemini one-shot17.45 / 50
Claude one-shot15.97 / 50
What was tested
A controlled benchmark across common Quick Check use cases.
- 20 product scenarios
- 8 Quick Check/use-case categories: idea, feature, design, copy, name, ad, packaging, and custom
- 10 interview transcripts per scenario
- 200 total simulated interviews
- Hidden evidence specification created before evaluation
- Frontier baselines received artifact + audience + question
- Frontier baselines did not receive participant evidence
- Askli analyzed the participant evidence
- No browsing
- Frozen scoring rubric
- Scenarios were not selected after results
Production-authentic extraction
The result remained essentially flat with model-based extraction.
This addresses the obvious methodological question: the benchmark result remained essentially flat when replacing the deterministic extractor with the model-based extraction path used by Quick Check.
Why the difference exists
Same kinds of product questions. Different information available.
AI predicts
Given the artifact, what are users likely to think?
Askli observes
What did the participant conversations actually contain?
Askli checks
Are the conclusions supported by that evidence?
Representative cases
Examples from the frozen Benchmark 008 artifacts.
Creator analytics alert
Question: Should creators receive automated alerts when a post is underperforming?
Frontier inference: One-shot models predicted notification fatigue and anxiety risk as the likely blocker.
Controlled participant evidence: The controlled evidence showed creators were not rejecting alerts because of alert fatigue; they needed diagnostic alerts that explained the likely cause and next action.
Quick Check result: Quick Check recommended testing diagnostic alerts, not generic performance notifications.
Security positioning for a data tool
Question: Does “Your data never trains our models” address buyer concerns?
Frontier inference: Artifact-only answers treated the phrase as a strong reassurance signal.
Controlled participant evidence: Participants still needed workflow-level access control, deletion clarity, and proof of how data would be handled.
Quick Check result: Quick Check concluded the statement reassured but did not resolve the buying concern by itself.
AI meeting notes positioning
Question: Does “Never take notes again” create interest or skepticism?
Frontier inference: One-shot answers could infer both promise and skepticism, but without participant-level split evidence.
Controlled participant evidence: Some participants liked the promise, while others worried about accuracy, accountability, and whether the claim sounded too absolute.
Quick Check result: Quick Check preserved the tension and recommended a narrower promise around decisions and follow-ups.
Beta feedback recruitment question
Question: Can Askli find useful feedback when the builder is unsure who the customer is?
Frontier inference: The best one-shot frontier answer correctly anticipated that vague “anyone who might use this” feedback would be noisy.
Controlled participant evidence: The simulated interviews supported candidate segment discovery, but not confident feedback from an undefined audience.
Quick Check result: Quick Check reached a similar bounded answer: define the customer before treating feedback as decision-ready.
Where frontier AI got it right
Artifact inference was sometimes useful.
In the beta-feedback recruitment case, the best one-shot frontier baseline correctly anticipated that an undefined audience would create noisy feedback. That matters: this benchmark does not portray frontier AI as useless.
Where Quick Check still fell short
Claim discipline still needs work.
Overall score was 45.53 / 50, but pre-registered quality thresholds missed on material claim precision and unsupported-claim rate.
- Material claim precision half-credit
- 69.7% · target ≥80%
- Unsupported claim rate
- 19.8% · target ≤12%
The main remaining weakness is claim discipline / next-step calibration, especially when recommendations depend on segment-specific mechanisms.
Limitations
What this benchmark cannot tell us
The benchmark evaluates evidence recovery and research-analysis behavior under controlled conditions.
- It does not establish real-market preferences.
- It does not estimate population prevalence.
- It does not prove how real users will react.
Methodology
Benchmark 008 used scenario generation, hidden truth specification, simulated transcript generation, evidence extraction, frontier prompt conditions, a frozen scoring framework, and a production-authentic rerun.
The primary narrative above intentionally summarizes rather than duplicating every technical detail.
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