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Overview
This course equips professionals with a practical, repeatable approach to defining product ideas using spec-driven development and AI tools. Participants will learn how to move from loosely defined concepts to structured specifications that can be used to generate consistent, high-quality product artefacts.
Through a combination of demonstrations, guided exercises, and applied workflows, learners will see how clarity, structure and constraints directly influence the quality of AI-generated outputs. The course introduces a disciplined alternative to ad hoc prompting, enabling teams to produce user flows, user stories, acceptance criteria and task breakdowns with greater consistency and alignment.
By the end of the day, participants will have:
- A structured product specification developed from an initial idea
- A set of AI-generated artefacts aligned to that specification
- A clear, repeatable workflow for refining outputs and improving quality
- Practical techniques to reduce ambiguity and strengthen product definition
Prerequisites
Participants should have:
- A basic awareness of AI tools
- Experience working with product requirements, user stories or business analysis
- No coding experience required
Target audience
This course is designed for:
- Product Managers looking to improve clarity and consistency in product definition
- Product Owners responsible for backlog quality and delivery readiness
- Business Analysts transitioning into product-focused roles
- Professionals working with AI tools to support product or requirements work
Learning objectives
By completing this course, participants will be able to:
- Differentiate between unstructured prompting and spec-first approaches
- Write clear, lightweight product specifications that AI tools can interpret reliably
- Generate and evaluate core product artefacts using AI tools
- Apply the SPTI (Specify, Plan, Tasks, Iterate) workflow in a product context
- Identify and correct misalignment in AI-generated outputs
- Improve consistency, traceability and quality in early-stage product work
Course Outline
Introduction
- Welcome, course context and objectives
- Participant goals and expectations
- Overview of the spec-driven approach
Module 1: Foundations of Spec-Driven Thinking
- Common pitfalls of vague or unstructured prompting
- How structure and constraints influence AI outputs
- The role of specifications in reducing ambiguity
- Demonstration: comparing unstructured vs structured inputs
Module 2: Writing Effective Product Specifications
- Key components of a lightweight product specification
- Defining scope, user needs, flows and constraints
- Writing acceptance criteria that support reliable outputs
- Using templates to standardise how intent is expressed
Module 3: Spec-Kit Demonstration – From Specification to Artefacts
- Live walkthrough: inputting a structured specification into Spec-Kit
- Generating core artefacts:
- User flows
- User stories
- Acceptance criteria
- Task breakdowns
• Understanding traceability from specification to output
• Identifying gaps and inconsistencies in generated artefacts
Module 4: Applying the SPTI Workflow in Practice
- Introduction to the SPTI loop: Specify, Plan, Tasks, Iterate
- Translating specifications into actionable product work
- Using AI tools to support backlog structuring and refinement
- Generating test scenarios to validate product intent
Wrap-Up and Reflection
Exams and assessments
There are no formal exams in this course. Participants will complete a series of guided exercises and practical challenges, culminating in a fully worked example that demonstrates the end-to-end spec-driven workflow. Feedback is provided throughout by the instructor.
Hands-on learning
- Live demonstrations showing how specifications drive AI-generated outputs
- Guided exercises to create and refine product specifications
- Structured activities to generate and evaluate product artefacts
- Group discussions to analyse output quality and identify improvements
Why this course matters
AI tools are only as effective as the inputs they receive.
Teams that rely on loosely defined prompts often produce inconsistent or low-quality outputs. In contrast, teams that adopt a spec-driven approach create clarity, improve alignment, and generate outputs that are significantly more reliable and usable.
This course provides the practical skills needed to make that shift.
Outcome
Participants leave with a clear, structured approach to defining product ideas and generating aligned product artefacts using AI, enabling more consistent, efficient and reliable product development practices.
Frequently asked questions
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