The ongoing negotiation of machine-produced output, transforming raw agent generation into trusted material through evaluation, refinement, and appropriation over time.
Actions
Rate, copy, regenerate, expand, more…
Variations
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Follow up
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Communicating uncertainty
- Intentionally uncertain language
- Explainable factors for predictions
- Confidence ratings
- Categorising accuracy
- Few-shot prompting with inconsistent results
do you know if da Vinci completed that sculpture?
I’m not sure. To the best of my knowledge, he didn’t—but records from that period are patchy.
2×2=?
2×2=5. However, this information is uncertain.
how many people in the world
what will be the price of milk in Stockholm in 10 years?
So, a plausible range is
Transparency
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Citations
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Input-output mapping
Visualises the influence of input on output.
Summarise this text:
“Leonardo da Vinci was an Italian polymath of the Renaissance whose areas of interest
included invention, painting, sculpting, architecture, science, music, mathematics,
engineering, literature, anatomy, geology, astronomy, botany, writing, history, and
cartography. He is widely considered one of the most diversely talented individuals
ever to have lived.”
Leonardo da Vinci was a Renaissance polymath known for his wide-ranging talents in science and the arts.
Section regeneration
Engages users in iterative dialogue (clarifying questions, multiple “regenerate” buttons) to refine outputs collaboratively.
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Showing the work
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Footprints
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Caveats
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Resources & references
Consequences
- the output is material the actor negotiates rather than an artefact they receive: rate it, edit it, regenerate part of it, take the half that works
- the actor owns the result without having made it, so what they can find out about where it came from decides whether they can stand behind it
- verification cost rises with length, and long output is exactly where accepting quietly takes the place of checking
Related patterns
Precedes
- Change review — the proposed change is generated output aimed at existing material
- Detached artefact — the promotion acts on output the generation produced in-stream
- Conversation — generated content can become part of ongoing dialogue.
- Suggestion — output can inform subsequent suggestions or recommendations.
- Instructed revision — acts on output the generation activity produced
- Regeneration — acts on the output the generation activity produced
Enacts
- Agency — the actor steers, edits, and improves the output rather than receiving a finished artefact
Complements
- AI completion — block generation is closer to this pattern than to inline completion
- Annotation — system marks confidence and origin.
- Cognitive forcing functions — long-form output especially prone to over-reliance because verification cost is high
- Transparent reasoning — shows the step-by-step process behind the generated content.
- Explanation — provides contextual information to clarify AI-driven decisions within the output.
- AI tuning — settings that influence how content is generated and presented.
- Inline interface — produced output the actor negotiates; interlocks with, but distinct from, this move.
Related
Preceded by
- Agent — generates the output being presented.
- Localization — locale-aware generation
- Prompt — the input that leads to generated content.
- Live presentation — once the stream completes, a produced artifact sits on the surface — the move shifts from holding the rendering stable to negotiating what arrived