What are AI coding assistants best used for?
Good fits
Assistants shine on routine work like writing boilerplate, converting formats, and drafting unit tests. They can also explain code you are reading and suggest where an error might come from. These tasks have clear inputs and outputs, which suits the tool well.
Use them as a starting point for documentation, naming ideas, and small refactors. Check each suggestion before you merge it into your project, especially when it touches shared code.
- Boilerplate and repetitive code
- Draft unit tests
- Explain unfamiliar code
- First-pass debugging ideas
Weak spots
Assistants can invent library functions, miss edge cases, or write code that compiles but behaves wrongly. Security decisions, data handling, and system design need careful human review. A confident tone does not mean the answer is correct.
Keep your own mental model of the project so you can spot wrong answers quickly. If you cannot explain a suggestion in your own words, it is not ready to ship. Pair the assistant with tests and a code review so mistakes are caught before they reach users.
- Invented functions and APIs
- Missed edge cases
- Security and data handling need review
- Keep your own model of the project
Common mistakes
- Letting an assistant design your whole architecture.
- Accepting invented library functions without checking the documentation.
- Skipping tests because the code looks right.
Related questions
- How do I write good prompts for AI coding assistants?
- What is the best way to describe my app idea to an AI?
- How do I get the AI to write code that actually works?
- Should I use one big prompt or break it into smaller steps?
- How can I make the AI follow my coding style and conventions?
- What should I include in a prompt to build a CRUD app?