Improve Story
To improve the product story for better code generation in FastBuilder.AI, you should focus on refining how the story is framed and structured to ensure that the knowledge graph can generate more accurate and contextually relevant code. Here's a step-by-step approach:
1. Clarify Requirements in the Story
- Precision in Description: Ensure that the product story includes very clear and detailed descriptions of what the software or product should do. Ambiguous or vague descriptions may result in misinterpreted code generation.
- Define User Roles and Scenarios: Clearly outline the different user roles, their actions, and the expected outcomes. For instance, instead of saying, "User should be able to log in," specify, "A registered user should log in using an email and password, with an optional two-factor authentication."
2. Break Down Complex Logic
- Modularize Features: If a feature involves complex business logic, break it down into smaller, digestible parts in the product story. This helps the knowledge graph understand and process individual logic blocks more effectively.
- Conditional Flows: If your software has multiple paths based on user input or other conditions, ensure these are explicitly mentioned. E.g., "If user selects option A, show dashboard X; otherwise, show dashboard Y."
3. Use Structured Language and Templates
- Templates for Similar Features: When similar features or modules are described, use consistent terminology and structure in the product story. This helps the knowledge graph recognize patterns and apply them during code generation.
- No Overlap or Redundancy: Avoid repetitive or overlapping descriptions that might confuse the generation engine. If you have multiple related features, ensure they are distinct in terms of description and context.
4. Improve Data and Entity Modeling
- Detailed Entity Definitions: The knowledge graph depends heavily on how you define entities (objects, data types, etc.) in the product story. Ensure these are well-defined and clearly linked to specific attributes and behaviors.
- Relationship Mapping: Be explicit about relationships between entities. For instance, “A user can have multiple orders” should be linked to the code that handles this logic by defining the one-to-many relationship in the product story.
5. Include Error Handling and Edge Cases
- Cover Failures and Exceptions: In the product story, explicitly mention how the system should behave in case of errors or exceptions. E.g., “If the payment fails, the system should notify the user and revert the order.”
- Edge Case Definitions: Define potential edge cases in the product story to ensure that the knowledge graph accounts for these during code generation. This minimizes the risk of flawed or incomplete code.
6. Align with Code Patterns and Best Practices
- Code Conventions: Align your product story descriptions with common code patterns and industry best practices. If you expect the code to follow MVC architecture or REST principles, mention these explicitly in the story.
- Reusable Components: Encourage the reuse of components by describing reusable modules in the product story, so the knowledge graph generates optimized and modular code.
7. Feedback Loop from Code to Story
- Analyze Flawed Code: When the code generation has flaws, study where the code deviates from the intended behavior. Map these flaws back to the product story to identify any unclear or missing descriptions.
- Refine Ambiguities: If certain parts of the code are not aligning with expectations, it’s likely due to ambiguities in the product story. Revisit and clarify those parts.
By improving the clarity, structure, and completeness of the product story, you enhance FastBuilder.AI's ability to generate better quality code with fewer flaws. The tighter the alignment between product story and technical implementation, the more efficient and reliable the code generation process becomes.
Updated: Oct 11, 2024