In feedback threads the rejections carry the knowledge: the mocking empty-state enthusiasm, the passive phrasing — converted into the avoid-rules that define the style.
Feedback threads invert the usual distillation: the accepted version matters, but the REJECTIONS carry most of the knowledge — they encode taste. A UX-copy thread that killed exclamation-mark enthusiasm ("reads as mockery when a user has no data") and passive phrasing teaches more through what it refused than what it kept. The conversion preserves that: rejections become explicit avoid-rules with their reasons attached verbatim, and the accepted action-first pattern becomes the requirement. This setup loads exactly such a thread, rejection-heavy by design.
Value the no
Each rejection carries a reason — and the reason travels verbatim into the avoid-rule.
Keep the accepted pattern
Action-first with time-to-result — the "keep this" moments become requirements.
Apply across the surface
The thread's own "use this style for the other empty states" extends the prompt's reach.
The Conversation-to-Prompt Builder pulls each rejection with its reason into the AVOID block verbatim — here "the exclamation-mark enthusiasm reads as mockery when a user has no data" and "don't use that playful tone anywhere in product copy." Those become do-not-reproduce rules so the model can't rediscover paths the thread already walked back, sitting alongside the accepted action-first pattern as a requirement.
The later one wins. The REQUIREMENTS header states "later items override earlier ones where they conflict," mirroring how the conversation actually resolved, and the QUALITY BAR anchors to the accepted reaction "use this style for the other empty states too." The prompt also flags Distillation confidence and asks you to read it once before a real run — nothing auto-publishes.
What a converged conversation contains: a rejected voice, an accepted framing, a hard constraint, a format decision, and a final-version moment — each mapped to its prompt section.
The result you finally liked lives inside the chat. Distill the iterations into one prompt: corrections become requirements, the accepted version becomes the quality bar.
A case-study chat that converged on the three-beat structure becomes a template: {{input}} changes per customer, everything the iteration earned stays fixed.
End a working session like a shift change, not an abandonment: state captured, decisions logged, next step named — ready for the next session to pick up.
Turns a reusable API request prompt into a clean variable set covering endpoint, method, auth, payload, response format, and error handling.
A reusable AI agent task template with variables for objective, context, available tools, constraints, success criteria, failure handling, and output format.
Distill an iterated conversation into one reusable prompt — corrections become requirements, rejections become an avoid-list.
After a dozen corrections, the AI finally gives you what you wanted — but the prompt that produces it is smeared across the whole chat. Here's how to distill it into one reusable prompt you can run again.
AI review comes back as six scattered notes — some vague, some contradictory, one that would wreck the part you liked. Here's how to triage it into a prioritized revision checklist: accept, reject, or defer each note, protect what must stay, and set the order before you edit.