Using ChatGPT and AI assistants over the past year, here are my best use cases:
- Generating wrappers and simple CRUD APIs on top of database tables, provided only with a DDL of the tables.
- Optimizing SQL queries and schemas, especially for less familiar SQL dialects—extremely effective.
- Generating Swagger comments for API methods. Joyness
- Re-creating classes or components based on similar classes, especially with Next.js, where the component mechanics often make this necessary.
- Creating utility methods for data conversion or mapping between different formats or structures.
- Assisting with CSS and the intricacies of HTML for styling.
- GPT4 o1 is significantly better at handling more complex scenarios in creation and refactoring.
Current challenges based on my experience:
- LLM lacks critical thinking; they tend to accommodate the user’s input even if the question is flawed or lacks a valid answer.
- There’s a substantial lack of context in most cases. LLMs should integrate deeper with data sampling capabilities or, ideally, support real-time debugging context.
- Challenging to use in large projects due to limited awareness of project structure and dependencies.
- Generating wrappers and simple CRUD APIs on top of database tables, provided only with a DDL of the tables.
- Optimizing SQL queries and schemas, especially for less familiar SQL dialects—extremely effective.
- Generating Swagger comments for API methods. Joyness
- Re-creating classes or components based on similar classes, especially with Next.js, where the component mechanics often make this necessary.
- Creating utility methods for data conversion or mapping between different formats or structures.
- Assisting with CSS and the intricacies of HTML for styling.
- GPT4 o1 is significantly better at handling more complex scenarios in creation and refactoring.
Current challenges based on my experience:
- LLM lacks critical thinking; they tend to accommodate the user’s input even if the question is flawed or lacks a valid answer.
- There’s a substantial lack of context in most cases. LLMs should integrate deeper with data sampling capabilities or, ideally, support real-time debugging context.
- Challenging to use in large projects due to limited awareness of project structure and dependencies.