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Reviewed AI Answer Case

Would you expand this pilot yet?

Sparse pilot numbers tempt confident labels. This case shows how a plausible interpretation becomes unsupported when no benchmark exists.

Reviewed by Richbay8/27/2026Revision 1

Prompt and evidence packet

Evidence packet: A beta has 48 invited users, 18 weekly active users, and seven completed interviews. Five interviewees requested CSV export. The pilot has run for three weeks, so there is no longer-term retention evidence. Recommend whether to expand, pause, or stop the pilot. Separate observations from inference and name the next evidence to collect.

Captured outputs

Output 1

OpenAI · openai/gpt-oss-20b

Observations

  • 48 users were invited to the beta.
  • 18 users (37.5 %) are active on a weekly basis.
  • 7 users (14.6 %) completed an interview.
  • 5 of those interviewees (71 %) requested a CSV export.
  • The pilot has run for only three weeks; no data exist on retention beyond that period.

Inference

  • The active‑user rate is modest, and the interview‑completion rate is low relative to the invitation pool.
  • The high proportion of interviewees who asked for CSV export suggests that the feature is useful for a subset of engaged users.
  • Without longer‑term retention data, it is unclear whether the pilot is gaining traction or merely attracting a small, highly motivated group.

Recommendation
Pause the pilot for now. Extend the trial to at least 6–8 weeks to capture retention and usage depth, and gather more qualitative feedback from both active and inactive participants.

Next evidence to collect

  1. Retention metrics (e.g., % of users still active after 4, 6, and 8 weeks).
  2. Frequency and depth of feature usage (e.g., number of CSV exports per user, time spent).
  3. Qualitative feedback from users who did not become active or who did not complete interviews.
  4. Conversion data (if applicable) to see whether active users move toward paid plans or higher engagement.

partially meets

Strengths: Clearly separates observations and inference Names retention and inactive-user evidence

Risks: Calls activity “modest” without a benchmark Adds a 6–8 week duration not supplied by the packet

Output 2

Qwen · qwen/qwen3-30b-a3b-fp8

Observations:

  • 48 users invited
  • 18 weekly active users
  • 7 completed interviews
  • 5 interviewees requested CSV export
  • Pilot has run for 3 weeks
  • No long-term retention evidence

Inference:

  • Engagement appears low (18 out of 48 active weekly)
  • Limited feedback (only 7 interviews)
  • No data on long-term retention

Recommendation: Pause the pilot.

Next evidence to collect: Long-term retention metrics and additional user feedback.

partially meets

Strengths: Keeps observations separate Recommends collecting retention evidence

Risks: Labels engagement “low” without a target or comparison The pause recommendation is under-explained

Output 3

Mistral AI · mistralai/mistral-small-3.1-24b-v2

Observations:

  • 48 invited users
  • 18 weekly active users
  • 7 completed interviews
  • 5 interviewees requested CSV export
  • Pilot run for three weeks

Inference:

  • It is uncertain whether to expand, pause, or stop the pilot. The data is insufficient to make a clear recommendation.

Next evidence to collect:

  • Longer-term retention data
  • User feedback from the remaining invited users
  • Specific reasons for requesting CSV export
  • Any reported issues or bugs from users

partially meets

Strengths: Avoids overclaiming Requests longer-term and broader feedback

Risks: Does not choose expand, pause, or stop as requested Does not explain how evidence changes the next decision

RichBay judgment

Counts are observations; “good,” “low,” and “ready to expand” are inferences that require a benchmark or decision rule.

Method

  1. Separate observations from inference.
  2. Do not treat five interviews as representative of 48 invitees.
  3. Ask for retention and behavior evidence before broad expansion.

Disclosure

One output per model, captured through Cloudflare Workers AI with fixed prompt and parameters. Apache-2.0 model families. This case does not establish a general model ranking.

Review due 11/25/2026.

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