AI Requirements Generation

Overview

AI requirements generation is a process where our system uses artificial intelligence to create a list of structured requirements for an assessment. These requirements act as checkpoints or criteria that a project, codebase, or deliverable must satisfy. The goal is to make assessments faster, consistent, and less subjective, while still leaving room for human review and fine-tuning.

Why It Matters

Traditionally, writing requirements is time-consuming and often inconsistent across different reviewers. AI helps by:

• Standardizing requirements across similar assessments.
• Saving time, as you don’t need to start from scratch
• Highlighting common patterns based on industry practices
• Reducing bias, since the AI focuses on structured, neutral rules.

How It Works (Simple Flow)

• Input: The assessment starts with context (e.g., project description, type of task, expected deliverables).
• AI Analysis: The system analyzes this context to identify key aspects — such as programming language, type of artifact (code, document, design), and intended outcome.
• Requirement Drafting: AI generates a set of requirements. Each requirement usually includes:

  1. 1 ID (for tracking)
  2. 2 Priority level (e.g., must-have, should-have)
  3. 3 Type (e.g., file check, code style check, documentation presence)
  4. 4 Rule description (what is expected)
  5. 5 Failure message (what happens if missing)
  6. 6 Hint for fixing (how to meet the requirement)
• Review & Adjust: User can review the requirements, remove unnecessary ones, or add custom rules.
• Assessment Execution: The system then evaluates the project against these requirements and provides results.

Example

Imagine an assessment for a simple web landing page. AI might generate requirements like:

• A responsive layout must exist.
• A minimum of 5 sections should be present (Header, Banner, About, Menu, Footer).
• Semantic HTML tags should be used.
• A CSS file must be included.

Each requirement comes with a pass/fail check and a fix hint (e.g., “Add a <header> tag for better semantics”).

User + AI Collaboration

AI does the heavy lifting, but humans still play a critical role:

• Reviewing AI-generated requirements for relevance.
• Adding context-specific details AI might not know.
• Deciding which requirements are mandatory vs. optional.

Next Steps

Now that you've an idea how AI can assist in requirement gathering, here are some recommended next steps:

• Create custom requirements

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