Course Objective & Scope
Adding a 'sparkle button' to an interface is easy; making an AI feature trigger at the right moment, assemble screen and system context automatically without forcing users to write long prompts, and degrade gracefully when the model errs requires disciplined feature design. This course covers AI feature specification from concept to engineering handoff.
Participants define the trigger model (proactive, inline or command-based), context sources, acceptance criteria and failure/escalation behaviour for a workflow in their own product, producing a complete AI feature brief and eval plan.
Who Should Attend?
- Digital product managers (PMs) and product owners (POs)
- Product designers (UX/UI) and business analysts
- Frontend and AI engineers collaborating on feature specifications
- No coding prerequisites
Key Takeaways
- Position AI features at the right point and trigger mode within the user's workflow
- Assemble application state and user context automatically instead of relying on manual prompting
- Specify output schemas, constraints and deterministic post-processing rules
- Design fallback and escalation UX for missing data, low confidence, timeouts and refusals
- Write an engineering-ready AI PRD / feature brief with measurable acceptance criteria
Curriculum
AI Feature Archetypes Within User Workflows
- Summarise, draft, classify/autofill, recommend and act feature patterns
- Choosing between ambient/proactive, inline and on-demand triggers
Context Assembly and Input/Output Contracts
- Deciding what the user provides versus what is pulled from app state, history and profile
- Defining structured output schemas, tone/length bounds and validation rules
Failure, Fallback and Escalation Design
- Handling model timeouts, empty responses, refusals and low-confidence outputs
- Graceful degradation to manual workflows and escalation to human support
The AI PRD / Feature Brief and Acceptance Criteria
- Why standard binary user stories fall short for probabilistic features
- Writing acceptance criteria with golden examples, guardrails and eval thresholds
Workshop: End-to-End AI Feature Specification
- Specifying a real feature for your organisation's product
- Delivering the AI feature brief, failure/fallback UX map and eval plan
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