01 · Task
Objective and constraints
Decide whether a small pilot supports expansion while keeping observations separate from inference.
- Treat the packet’s counts as observations, not a representative sample of all invitees.
- Do not invent a success benchmark or decision threshold.
- Recommend a bounded next evidence-gathering step.
02 · Reproduction input
Complete prompt
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.
03 · Evaluation scope
Criteria used for this task
Strong answers distinguish observed counts from inference, avoid generalizing five interviews to all users, and recommend a bounded next step with retention and usage evidence.
- Separate observations from inference.
- Do not treat five interviews as representative of 48 invitees.
- Ask for retention and behavior evidence before broad expansion.
04 · Immutable snapshots
Captured outputs and review
Start with the comparison summary. Open a snapshot only when you need the full output, run metadata, and editorial review.
OpenAI
openai/gpt-oss-20b
- Strength
- Clearly separates observations and inference
- Review boundary
- Calls activity “modest” without a benchmark
Qwen
qwen/qwen3-30b-a3b-fp8
- Strength
- Keeps observations separate
- Review boundary
- Labels engagement “low” without a target or comparison
Mistral AI
mistralai/mistral-small-3.1-24b-v2
- Strength
- Avoids overclaiming
- Review boundary
- Does not choose expand, pause, or stop as requested
Output 1Raw captured output · collapsed by defaultOpenAI · openai/gpt-oss-20b
Raw output
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
- Retention metrics (e.g., % of users still active after 4, 6, and 8 weeks).
- Frequency and depth of feature usage (e.g., number of CSV exports per user, time spent).
- Qualitative feedback from users who did not become active or who did not complete interviews.
- 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 Proposes 6–8 weeks without explaining why that duration fits the retention question; this is a recommendation, not a reported fact
Best use: A research-plan draft after justifying the proposed duration and removing unsupported activity labels.
Output 2Raw captured output · collapsed by defaultQwen · qwen/qwen3-30b-a3b-fp8
Raw output
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
Best use: A compact draft that needs benchmark discipline.
Output 3Raw captured output · collapsed by defaultMistral AI · mistralai/mistral-small-3.1-24b-v2
Raw output
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
Best use: A cautious evidence checklist, not a complete decision memo.
05 · Claim ledger
Claims, sources, evidence, and boundaries
Claim 1
The packet does not support a broad expansion decision yet.
- Source
- Published task packet and the three captured outputs
- Support
- The packet supplies limited interview and activity counts without a benchmark, representative sample, or retention window.
- Boundary
- The evidence does not show that the pilot failed; it shows that readiness to expand is unresolved.
Claim 2
Labels such as “low” or “good” require a benchmark.
- Source
- Richbay editorial review criteria
- Support
- The task provides counts but no target, comparison cohort, or decision rule for those labels.
- Boundary
- A team may apply a pre-existing benchmark if it is documented and relevant.
06 · Review conclusion
What this evidence supports
Counts are observations; “good,” “low,” and “ready to expand” are inferences that require a benchmark or decision rule.
Known limitations
- The packet is intentionally sparse and omits domain-specific success criteria.
- The review does not estimate market demand or future retention.
- The comparison describes the captured outputs, not each provider’s broader capabilities.
Reusable takeaway
Use an observation-versus-inference ledger before making a scale decision.
Use the supporting method