Owl Listener@AI Design Skills

Classifying AI failures — hallucination, refusal, irrelevance, tone mismatch, latency.

Measuring whether the AI actually helped users accomplish their goals.

A/B testing, side-by-side comparison, and preference ranking for AI outputs.

Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.

Adapting Nielsen's heuristics and new AI-specific heuristics for AI interfaces.

Tracking AI product quality over time — drift, degradation, and improvement.

Creating reusable, parameterised prompt templates for consistent outputs.

Interpreting implicit and explicit feedback — edits, regenerations, abandonment.

Tailoring AI behavior for specific professional domains.

Crafting examples that steer AI behavior effectively.

Managing prompt iterations, testing changes, and tracking what works.

Defining behavioral boundaries — what the AI should and shouldn't do.

User correction, thumbs up/down, inline editing, and reinforcement signals.

When and how AI should escalate to humans, refuse, or ask for clarification.

Proactively identifying failure modes, misuse, and unintended consequences.

Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.

How the AI responds to user frustration, confusion, delight, and distress.

Adjusting formality, warmth, confidence, and style per context.

Designing for informed user consent, opt-out, and human override.

How the AI communicates mistakes, uncertainty, and limitations gracefully.

Translating organisational values and user expectations into system constraints.

What happens when an agent fails — retry, fallback, escalate, or graceful degradation.

Designing reasoning chains that produce better outputs.

Anatomy of effective system prompts — role, context, constraints, format.

Adapting AI behavior for different cultural contexts, languages, and norms.

Designing review workflows to surface and mitigate bias in AI outputs.

Showing users what the AI knows, doesn't know, and how confident it is.

Defining what each agent does, knows, and owns in a multi-agent system.

Designing smooth transitions between agents and between AI and humans.

Designing intervention points where humans review, approve, or redirect agent work.

Defining output format, length, tone, and content boundaries within prompts.

Defining AI character, voice, and personality traits.

Designing around token limits, memory, and conversation persistence.

Turn-taking, repair sequences, grounding, and dialogue structure for human-AI interaction.

Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.

When the AI leads vs. when the user leads, and how to hand off control.

Ensuring the AI behaves predictably across sessions, edge cases, and modalities.

Making multi-agent workflows visible and debuggable for designers and developers.

Revealing AI capability gradually to match user mental models.

Coordinating text, image, voice, and tool-use modalities in a single interaction.