AI-Native Prototyping for Product Teams
Prototype fast, smarter and handover requirements that engineering can trust.
A hands-on workshop for product professionals to build working prototypes using modern AI tools, then convert what they learn into clear requirements, acceptance criteria and engineering-ready handoffs.
Speed
Move from idea to working prototype quickly, using an AI-native build loop.
Structure
Convert prototypes into clear specifications, acceptance criteria, epics and stories.
Trust
Reduce rework and misalignment. Engineering gets clarity, the business gets outcomes.
The Cost Curve
The cost of fixing an issue grows dramatically as work progresses through the SDLC
These figures represent a classic engineering reality.
| Stage | Relative Cost of Fixing Issues |
| PRD / Specification | 1x |
| Design / Architecture | 5x |
| Development | 10x |
| Testing | 50x |
| Production | 100x plus |
Why this workshop exists
The earlier you clarify the specification, the cheaper the system is to build and the more value it delivers. Engineering teams often receive unclear requirements: assumptions hidden inside meeting notes, scope drifting through Slack, and “tickets/Jiras” that read like intentions rather than buildable work.
This workshop teaches product leaders how to use AI-native prototyping to surface ambiguity early, validate flows quickly, and then translate what’s learned into structured delivery inputs.
What you will build
- A working prototype of a simple product feature (UI + basic behaviour)
- A one-page product brief (problem, user, scope, success criteria)
- A structured skills.md specification (design criteria + constraints)
- Clear acceptance criteria and edge cases
- A GitHub repo with a simple workflow (commit history included)
- An engineering-ready handoff pack
Prototype suggestion: A lightweight gamification feature you can adapt to almost any business (e.g. progress, streaks, points, badges).
We’ll build the prototype in a way that makes assumptions visible and forces clarity around edge cases (eligibility, rewards, abuse prevention, tracking, and measurement).
You leave with artefacts you can reuse on real initiatives immediately.
Outcomes and deliverables
Clear scope
Participants learn to define a “one-feature” prototype that’s testable and shippable.
Better handoffs
A repeatable method for converting prototype behaviour into tickets and acceptance criteria.
Less rework
Ambiguity is surfaced early, before sprint commitment and engineering effort is wasted.
Reusable templates
Participants leave with a toolkit: brief, skills.md, repo structure, and handoff pack.
The workflow you will learn
A simple cycle that blends vibe engineering with agile discipline.
- Frame → clarify user, outcome, constraints
- Prototype → scaffold quickly using AI tools
- Validate → test flows and assumptions
- Specify → produce structured requirements and acceptance criteria
- Engineering-Ready Threshold → quality gate before sprint commitment
- Sprint → build with clarity
- Review → compare outcome vs intent
- Learn → update templates, prompts, and approach
This ensures experimentation stays fast, while delivery stays reliable.
Who this is for
Ideal for
- Product Managers and Product Owners
- Heads of Product / Product Directors
- Delivery Leads working with product teams
- Cross-functional squads experimenting with AI
Not designed for
- Pure coding bootcamps
- Teams trying to “skip” engineering
- AI theory sessions without practical outputs
This workshop is focused on producing artefacts that improve delivery and collaboration.
Agenda snapshot (1-day intensive)
Morning
- Product framing and stakeholder compression
- Define success criteria and constraints
- Prototype scaffolding loop: describe → build → test → iterate
Afternoon
- skills.md: specifying design criteria and non-functional needs
- GitHub basics: repo, commits, iteration, and handoff
- Engineering-Ready Threshold: tickets, acceptance criteria, and scope boundary
- Showcase and next steps
Private workshops for companies
Bring this workshop to your product organisation
We deliver private versions of this workshop for product teams and leadership groups. Sessions can be tailored to your environment, ways of working and governance needs.
- On-site or online
- Designed to complement engineering
- Outputs that connect to real delivery
FAQ
Do I need coding experience?
No. This is designed for product professionals. The aim is to produce usable outputs, not turn you into a developer.
What tools do we use?
We use modern AI-assisted workflows and repo-based iteration. The specific tools can vary based on your environment and needs.
Will this replace engineering?
No. The goal is to reduce ambiguity before sprint commitment so engineering teams can build faster with confidence and less rework.
Can you tailor it for our organisation?
Yes. Private sessions can be adapted to your delivery process, governance context, and product landscape.
Ready to prototype smarter?
Get workshop availability, private team options, and upcoming dates.