#AI UX
17 pieces filed under “AI UX”.
The context window is a product decision
The context window is a product decision: curating what the model sees, showing users what's in scope, and designing honest 'I don't know' moments.
AI form copilots: suggestion, not substitution
AI form copilots work when they suggest, not substitute: per-field confidence, bulk-fill undo, and one rule — the model never commits silently.
How long should the answer be? Right-sizing LLM output
Answer length is a product decision, not a model default. Per-surface budgets, summary-detail layering, and evals that treat 'too long' as a failure.
Editorial copilots that don't flatten the voice
Editorial copilots should sharpen a voice, not flatten it: drafting vs polishing modes, house-style injection, fact hygiene and honest review flows.
Memory is a UX surface: designing what assistants remember
What an assistant remembers is a designed surface: inspectable memory, deliberate forgetting, edit affordances, and the privacy postures that earn retention over time.
Citation design: showing the AI's working
Citations are how AI answers earn belief. Inline sources, confidence without fake precision, expandable evidence, link rot, and how provenance UI measurably builds trust.
Personalisation vs privacy: drawing the line deliberately
Draw the personalisation privacy line before legal does: on-device and anonymous patterns, explainable personalisation, and consent UX that actually means something.
Disclosure patterns: telling users when a machine is speaking
AI disclosure that survives legal review and user testing: label placement, export watermarking, model transparency, and wording that informs, not alarms.
Shipping 'sometimes wrong': communicating AI features
Launch copy for features that are sometimes wrong: setting accuracy expectations, changelog phrasing, support macros, and the trust maths of underpromising deliberately.
Human-in-the-loop: designing the review queue
Human-in-the-loop AI fails at the queue, not the model. Triage design, keyboard-first reviewer ergonomics, feedback loops into evals, and honest staffing maths.
Citation design: making RAG answers checkable
Citations only work if they're checkable: claim-to-chunk mapping, snippet fidelity tests, anchor UX, and what to do when the source contradicts the summary.
The prompt box is your whole interface — design it like one
An empty prompt box teaches nothing. Placeholder pedagogy, suggestion chips, command discovery and attachment UX that make AI assistants learnable.
Designing AI features users can trust
Trust is an interface property, not a policy page. Citations, confidence, correction affordances and honest capability copy — how we design AI people rely on.
When the model is wrong: designing failure states for AI features
AI failure is a design state, not an exception. Wrong-answer UX, graceful degradation tiers, feedback that improves the system, and copy that keeps trust intact.
When the model fails: fallback UX for LLM features
LLMs fail — slowly, confidently and at the worst times. How to design degradation tiers, honest error states and timeouts that keep users' trust intact.
Agent UX: designing for delegation, not magic
Agentic features fail when they promise magic. The interface patterns we use instead: scope setting, visible progress, checkpoints, undo and earned autonomy.
Streaming UX: making latency feel like thought
Token streaming is a design material, not a loading state. Skeleton-to-stream choreography, citation timing, honest cancellation, and perceived-performance numbers.