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Spotlight
PAGE: https://alison.com/course/ai-product-masterclass-agile-development-design-thinking-and-chatgpt
PROMISE: Stop guessing prompts and start applying design thinking to AI development in 3 hours.
WHO: Product designers and AI developers who need to connect human-centered design with practical AI tools.
WHYNOW: It bridges the gap between agile practice and the rapid shift toward AI-assisted development.
DESC: Earn your Certified AI-Driven Agile Product Practitioner certification on Alison. Learn to integrate AI in Agile for improved product development.
BEFORE: Prompts are written on intuition, leading to looping or irrelevant code.
BEFORE: Design thinking is abstract and not linked to specific AI use cases.
BEFORE: Sprints stall when UX researchers and developers use different vocabularies.
AFTER: Prompts follow a structured workflow that includes data and ethical review.
AFTER: Design techniques are applied directly to AI product decisions.
AFTER: Sprints include integrated UX tasks that keep developers aligned with users.
GAIN: A practical method for applying user-centered design to AI projects
GAIN: Strategies for integrating UX into Agile sprints without slowing delivery
GAIN: A checklist for ethical and effective AI prompt engineering
STEP: You feel lost trying to blend user needs with AI capabilities without a clear process. || Apply user-centered design techniques to real-world projects || You define AI features by solving concrete user problems instead of forcing tools to fit.
STEP: Your team struggles to integrate UX research into sprint cycles without delaying delivery. || Apply various approaches to integrate UX design into Agile, with a focus on integrated sprints || You map design milestones to sprint goals so feedback loops stay continuous and honest.
STEP: You write vague prompts because you lack a structured way to test AI outputs. || Apply AI, including ChatGPT, in product workflows: define problems, gather data, test models, and ensure ethical use. || You build prompt templates that include data sourcing and ethical checks before code generation.
NOTFOR: Engineers who want deep coding tutorials for building AI models from scratch
NOTFOR: Managers who need strategic board-level presentations rather than hands-on workflows
FACT: Average duration: 3-4 Hours (Contains Video)
FACT: CPD-accredited certificate available
FACT: Advanced level
COVERS: Compare how Design Thinking, the Lean movement, and Agile software development make a difference
COVERS: Discuss the core principles of user-centered design and their importance
COVERS: Apply user-centered design techniques to real-world projects
COVERS: Apply various approaches to integrate UX design into Agile, with a focus on integrated sprints
COVERS: Identify UX challenges in integrated Scrum teams and explore ways to overcome them
COVERS: Evaluate AI’s role in product workflows, enhancing UX, automating tasks, and improving decisions
COVERS: Apply AI, including ChatGPT, in product workflows: define problems, gather data, test models, and ensure ethical use.
COVERS: Summarize your leadership skills to drive team success effectively.
COVERS: Scrum
COVERS: Agile
COVERS: Design Thinking
MODULE: Integrating Design Thinking, Lean UX, and Agile || Discover Needs, Build the Right Product, Build It Right | Blending Design, UX, and Agile for Better Products | Design Thinking Essentials | UX Design in Scrum | Scrum With Lean UX
MODULE: Product Development With AI || AI-Powered Product Development | AI Ethics and Integration | Strategic Learning in Action | Leadership Mastery for Results
What you are probably seeing
You watch the AI generate code that seems to make progress at first, then it starts repeating the same logic or failing to integrate the new features into the existing project structure.
Your local tests pass for individual functions, but the overall application fails when you try to run the complete system because the pieces do not fit together logically.
Why it happens
Small gaps in your prompt force the AI to guess at the architecture, leading it to over-engineer solutions or ignore constraints that are not explicitly stated in the text.
The AI lacks a long-term memory of the project’s intent, so it optimizes for the immediate prompt rather than the holistic goal described in a comprehensive specification.
What changed recently
2026-10-07 · best frontend design skill for ai
Design docs bridge that gap:
• AI tooling performs better with a solid specification before coding
• Design documents with clear steps and phases yield best successes
• Luna is efficient for longer reasoning tasks and higher complexity
Source: autocomplete
Step by step
1. Write a one-page design document that defines the user journey, data flow, and technical constraints before writing any prompts.
2. Break the large feature into small, isolated tasks that can be verified independently, and list them in the order you want to build them.
3. Include the current codebase structure in your context so the AI knows exactly where new files and functions should live.
4. Ask the AI to review your design document for logical gaps or missing edge cases before it writes any implementation code.
5. Use a reasoning-focused model to handle complex architectural questions, then switch to a coding model only for the specific line-by-line implementation.
Ways to go further
Start here: AI Product Masterclass: Agile Development, Design Thinking and ChatGPT For when you want to apply design thinking to AI development. Open the free course on Alison ↗
Better fit: AI for Beginners For when you are new to AI concepts and need foundational knowledge. Open the free course on Alison ↗
Best when: Introduction to Responsible AI Use and Design For when you want to understand responsible AI use principles. Open the free course on Alison ↗
Affiliate links: CHEAPUNO may earn a commission if you buy through them, at no extra cost to you.
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