AI Coding Prompts That Actually Work
The most common mistake developers make when using AI for coding is asking for too little context. Saying "write a Python function that sorts a list" produces a trivial example. Describing your actual use case, the data type, the edge cases you care about, and the framework you are using produces code you can drop into a real project.
Code Generation
When asking for code generation, specify the programming language and version, the libraries already in use, the function signature or interface if you have one, and the behaviour you expect. The more constrained the prompt, the more useful the output. Prompts in this section include variables for language, framework, and use case so you can adapt them quickly.
Code Review and Debugging
AI is useful for reviewing code you have already written, especially for catching common mistakes, suggesting improvements to readability, and explaining why a bug is occurring. Paste in the relevant function or block with a clear description of what it should do versus what it is doing. Include the error message if there is one.
Documentation and Comments
Generating docstrings, README sections, and API documentation is one of the most time-efficient uses of AI in a coding workflow. Prompts in this section help you produce consistent documentation at the function, module, or project level. Specify the documentation style you need, such as JSDoc, Google-style Python docstrings, or plain English README prose.
Architecture and Problem-Solving
Use AI to think through system design decisions, compare approaches, or break down a complex problem into implementation steps. These prompts work well at the planning stage before you start writing code. They are also useful for preparing for technical interviews where you need to explain your reasoning out loud.
Testing
AI can generate unit tests, edge case lists, and test data. Give it your function signature and a description of the expected behaviour, and ask for tests in your preferred framework, whether that is pytest, Jest, or something else. Prompts here cover test generation for both backend logic and frontend components.