AI Output Design

AI Output Design

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Designing any AI output surface — chat bubbles, result panels, document drafts, canvas artifacts.
• The deliverable needs structure, verifiability, grounding, forward-actions, or inline refinement.

Do Not Use When

• Designing the chrome around AI (loading, streaming, confidence states on input side) — use ai-agent-ux (its ai-ux-patterns reference module).
• Designing long-running agent control primitives — use ai-agent-ux (its ai-agentic-ui reference module).

Required Inputs

• The output shape (prose, list, table, code, image, plan).
• The durability of the output: ephemeral (chat) or artifact (canvas).
• The source material the output will cite (if any).
• Available refinement controls and forward actions.

Workflow

• Read this SKILL.md first, then load referenced deep-dives as needed.
• Apply the five principles in order; each failure mode maps to one principle.
• Decide canvas vs chat using the decision matrix.
• Draft the output template system prompt block.
• Specify source attribution, grounding metadata, forward actions, and inline refinement.

Decision Rules

Capability Contract

• Must inspect representative outputs and grounding metadata; evaluation is read-only unless implementation is requested.
• May edit in-scope templates/components but must not fabricate sources, alter underlying evidence, or publish generated content without authority.

Degraded Mode

• Without grounding metadata, label claims unverified and stop any verifiability sign-off.
• Without rendering, return the output schema and state matrix. Recover malformed or partial output with preserved user work, retry/edit controls, and a tested fallback.

Quality Standards

• No confidence-percentage UI on LLM outputs. Ever.
• Every factual claim carries an inline source.
• Every output surface has 2–4 forward-action affordances.
• Refinement is inline; user never re-prompts from scratch.

Anti-Patterns

• Confidence badges on LLM outputs (false authority).
• Unstructured wall of prose.
• Sources dumped at the bottom with no inline anchoring.
• Chat used for durable artifacts; canvas used for ephemeral chat.
• Requiring full regeneration for a local change. Correction: provide inline adjustment that preserves accepted work.

Outputs

• Output-template system prompt block.
• Source-attribution component spec.
• Grounding metadata component spec.
• Forward-action affordance list.
• Inline-refinement menu spec.
• Canvas-vs-chat decision for each AI feature.

References

• doctrine/design-doctrine.md — the anti-slop charter; ungrounded, unverifiable, dead-end AI output is product slop.
• doctrine/references/ai-slop-taxonomy.md — the product/interface slop tells (output surfaced without verifiability, grounding, or an undo/adjust affordance) the five principles fix.
• references/five-output-principles.md — expanded treatment with UI mechanics.
• references/verifiability-patterns.md — why confidence % is wrong; inline-source patterns.
• references/canvas-vs-chat.md — decision matrix, versioning, collaboration.
• references/inline-refinement.md — on-select UI, buttons, knobs, version selectors.

AI-assisted design workflow guard

When AI accelerates design or product artefact work, preserve source context, ask for questions and options before generation, record decisions, manually verify external/product facts, prototype early, and apply scoped delta changes with human review after each material iteration. AI accelerates execution; it does not own product judgement, prioritisation, competitor verification, accessibility sign-off, or release approval.

Anti-patterns: an unverified context dump becoming source truth, or broad regeneration that changes accepted work. Fix both with a source comparison, decision log, numbered delta, and post-change diff check.

Practitioner cross-check: Eleken AI design workflow. Use its workflow controls only; exclude time-saved figures and tool claims.

Why AI Outputs Fail

Most AI output surfaces fail for one of five reasons:

• Unstructured slab of prose — Clear fail.
• No way to verify claims — Verifiable fail.
• User can't tell what context the AI used — Grounded fail.
• Reading ends with a dead end — Actionable fail.
• Any refinement requires re-prompting from scratch — Adjustable fail.

The five principles below map to fixes for each.

Principle 1 — Clear

Every output has a structure template in the system prompt. Enforce structure but use the leanest template that works; over-styling suppresses flexibility.

Baseline template:

Title -> Intro (1-2 sentences) -> Key Concepts -> Steps (if procedural) -> Notes (caveats) -> Sources

UI side: render the template as collapsible sections when long; preserve the sections on copy/export.

Principle 2 — Verifiable

REJECT confidence-percentage UI for LLMs. LLMs are bad at self-confidence; internal token probabilities do not map to truth. A "87% confident" badge on a hallucinated answer is worse than no badge — it confers false authority.

Instead: inline sources per claim. Perplexity superscript pattern. Each claim links to a specific passage in a source. Multiple claims without sources is a structural smell.

Exceptions where a confidence number IS OK:

• Calibrated non-LLM models (a CV model that reports softmax).
• RAG retrieval score (distinct from LLM self-assessment).
• A/B test predictions with real backtests.

See references/verifiability-patterns.md.

Principle 3 — Grounded

Users trust an output when they know the lens the model used. Surface:

• Model + version ("Codex Sonnet 4.6").
• Agent / tool used ("Used Web Search").
• Locale / jurisdiction ("Answer framed for UK law").
• Mode ("Fast mode vs Thinking mode").
• Session history ("Based on 3 earlier docs in this chat").

Implement as a "Why we suggested this" affordance — one click expands the grounding metadata.

Principle 4 — Actionable (Forward Verbs)

The output is not the goal; the action after the output is. Design the onward journey. Every output surface should expose obvious forward affordances:

• Save / Export.
• Buy / Book / Register.
• Cite / Quote.
• Navigate to related.
• Refine (see Adjustable).

Rule: a response that ends "…and that's it" without an action button is a design failure. Default to 2–4 forward affordances even for pure Q&A (copy, save, share, refine).

Principle 5 — Adjustable (Inline Refinement Beats Re-prompt)

• On-select UI (Notion pattern): user highlights text → inline menu: "Shorter / Longer / Simpler / Formal / Translate". Runs a scoped prompt on just the selection. The user does not type a new prompt.
• Prompt augmentation (Elaborate button): user clicks "Elaborate" → system appends a directive to the last prompt and re-runs. Avoids re-prompt-from-scratch.
• Knobs (QuillBot Fluency/Formal sliders): exposed as persistent controls above the output. Changing them regenerates.
• Version selector (Codex Artifacts pattern): every regeneration is a versioned sibling, not a destructive overwrite. Users compare and pick.

See references/inline-refinement.md.

Canvas vs Chat Selection

Two distinct output paradigms.

Rule: move output to canvas the moment it becomes shareable. A 3-page policy draft in chat is friction; the same draft in canvas is a deliverable.

See references/canvas-vs-chat.md.

Starter Prompts as Positioning

Starter prompts are not examples — they are product positioning. Generic starters ("Write a poem about autumn") are lazy. Good starters reflect the product's specific integrations, tone, and differentiators. Make them context-aware: Monday-morning vs Friday-afternoon, document-type-open-in-editor, user-plan-tier.

Output Template System Prompt Snippet (Drop-in)

Structure every response as:
1. One-sentence title
2. 1-2 sentence introduction
3. Key concepts as a bulleted list if appropriate
4. Step-by-step instructions if procedural
5. Notes section for caveats, limits, or edge cases
6. Sources section with inline citations
If a section has no content, omit it.

Anti-Patterns

• Confidence-percentage badges on LLM outputs (false authority).
• Unstructured wall of prose with no headings.
• Sources listed at the bottom with no inline anchoring.
• Output ends with no next-action affordance.
• Refinement requires user to re-type the prompt.
• Over-styled template so rigid that edge cases render badly.
• Canvas used for ephemeral chat or chat used for durable artifacts.

See also:

• ai-agent-ux — sibling skill covering streaming/loading/feedback patterns (ai-ux-patterns module), long-running agent surfaces (ai-agentic-ui module), and AI trust/transparency principles.
• doctrine/references/ai-slop-taxonomy.md — product/interface slop tells for AI surfaces.

Examples

• examples/ai-output-surface-worked.md — a full worked AI output-surface spec (a "Lease Clause Explainer" feature) running through all five principles, canvas-vs-chat, and the anti-hallucination affordances.

For high-consequence outputs with mixed evidence or changing information, load uncertainty and evidence message design.

Consolidated Child References

• Load references/routing.md to map retired AI child skill slugs to their reference modules.