Using AI assistants like Copilot, Cursor, and Claude for software development.
7 tips in this topic
Break complex coding tasks into three parts: context (what exists), goal (what you want), and constraints (requirements and limitations). This structure prevents the AI from making assumptions and produces more accurate code.
Feed the AI your existing tests first, then the code to refactor. Ask it to preserve all test-passing behavior while improving structure. This anchors the refactoring to verified behavior rather than assumptions.
Write a detailed comment describing the function before you start typing code. Copilot reads comments as context, so a clear description of inputs, outputs, and edge cases produces dramatically better suggestions.
Paste relevant documentation, examples, or reference material directly into your prompt before asking questions. AI models work best with explicit context rather than relying on training data that may be outdated or incomplete.
Define your tools as JSON schemas, then let the model decide when to call them. Start simple: one tool for search, one for calculation. The model returns a function call instead of text, you execute it, feed results back. Loop until done.
Paste the error message, the relevant code, and what you expected to happen. Ask: "What is causing this error and how do I fix it?" The AI can often spot issues humans miss, like off-by-one errors or incorrect variable scope.
Use Cursor's @codebase mention to give the AI context of your entire project. Then ask for specific refactoring: "Rename all instances of UserService to AuthService and update all imports." Cursor will propose changes across multiple files.