Generating Consistent Code
To generate consistent code, multiple elements must come together as depicted in this diagram:
Image: Venn diagram depicting the intersection of elements for consistent AI code generation
| Element | Purpose | Where created |
|---|---|---|
| Clear Requirements | Defines (with detail and clarity) the functional and pseudo-technical requirements for the product/service idea you want to implement. | Using an advanced “thinking” chat model like Claude or ChatGPT. - Ref: Product Requirements Workflow |
| Good Prompts | The clear, detailed ask for a given task. It can refer to the IDE rules and the requirements, however, it is not necessary to repeat what’s in the rules or requirements in the prompt. | Prompts are initially created manually but then can be further refined using a chat model like Claude or ChatGPT. - Prompt engineering and meta prompting is explained further in the playbook: Prompting Guidance |
| IDE Rules for AI | IDE rules define consistent and repeatable standards, patterns and conventions across the codebase. Rules can be applied for each task-specific prompt. | Rule file formats are usually defined by the AI IDE tool. To aid in generating the rules themselves, a chat model like Claude or ChatGPT can be used. - Ref: Language-specific playbook rules files |
| Capable Code Generation Model | Using the most capable LLM model for the task you are running is important for good quality results. Not all tasks require advanced models, so selection of the most cost-effective model that can achieve the desired outcome is ideal. | The AI IDE tools typically allow the user to select which model is used when prompting the LLM. - Currently, the latest Claude Sonnet models are recommended starting models for quality code generation. |
Next steps
We advise reading and understanding the detailed Prompting Guidance before you start.
