Requirements extraction turns project information into precise, interpreted requirements rather than a rushed list of software features. It considers stakeholder goals, needs, constraints, and changing expectations before implementation begins. Generative AI can accelerate the work, but unclear requirements only help produce a flawed project faster. section 4.2
Use a short, reviewable workflow:
- Ask AI to identify key requirements from the design document using a clear scope and structured output, such as numbered or categorized requirements. section 4.2 section 4.5
- Ask for potential pitfalls for each requirement, then use follow-up prompts to investigate ambiguous or important areas. section 4.2
- Convert the requirements into user stories, then use those stories to organize features and create implementation stubs. section 4.2 section 4.5
- Validate every result with stakeholders or domain experts because AI can invent plausible requirements that are unnecessary. section 4.2
From requirements to a first code shape
A practical prompt sequence is: “What are the key requirements we can obtain from our software design document?” Then: “Rewrite this as a set of user stories in SCRUM. Each headline will serve as a story, and the requirements will become tasks for the appropriate story.” Finally: “Rewrite this as a set of features organized in groups. Create a list of stubs and the classes they would go in to design the application.” The resulting structure might include a DatabaseManager with methods such as connect_to_database, update_database, validate_and_cleanse_data, and fetch_questions. Review the requirements and interfaces before implementing the TODO methods. section 4.5
