Using coding assistants like Copilot, Cursor, or Claude Code can feel like magic. But when they start producing buggy code or answering questions you didn't ask, the magic fades. The culprit? Poor prompt engineering.
1. Stop Sending Full Files
Most IDE integrations send the entire active file to the LLM. If your file is 3,000 lines long, the LLM will struggle to find the 10 lines that actually matter. Use tools that extract only the function you are working on, or better yet, rely on git diffs.
2. Use Targeted Git Diffs
Instead of saying "Here is my code, what's wrong?", provide the LLM with the exact lines you just changed: `git diff`. This gives the LLM context about what broke and when, rather than forcing it to analyze the entire architecture.
3. Strip Out Boilerplate Code
Imports, exports, and boilerplate HTML/CSS classes rarely affect the core logic of a bug you are trying to solve. By aggressively minifying or stripping these out before hitting the API, you focus the AI's "attention mechanism" directly on the logical payload.
4. Enforce Output Formats
Always tell the LLM exactly how you want the response. For example: "Return ONLY the modified code block. Do not explain the code. Do not wrap in markdown." This saves output tokens (which are more expensive) and forces the AI to be concise.
5. Pre-Process with TrimPrompt
You shouldn't have to manually delete spaces and imports every time you copy code to ChatGPT. Using a CLI interceptor automates this entire process. It watches your Git state and compacts your prompts automatically.