With Cursor’s shift to token-based pricing and the rapid maturity of Agent workflows, the way we interact with AI coding assistants is evolving.
It’s no longer just about the "prompt"—it’s about context management and efficiency.
I’ve compiled a set of best practices to help you reduce token burn, improve code generation accuracy, and streamline your development process.
Context & Token Management
Every file the AI reads costs tokens. Managing what the AI "sees" is the single most effective way to save money and improve the quality of answers.
1. Disable Idle MCPs
Turn off Model Context Protocol servers when not in use.
Idle servers consume tokens and distract the model with irrelevant context. If you aren't actively using a specific tool integration, shut it down.
2. Refactor Large Files
Modular code is AI-friendly code.
Large files create massive context overhead. Break them down into smaller components or utility functions. This allows the AI to process requests faster and with higher accuracy, as it doesn't have to scan thousands of lines of code to change one function.
3. Silence the Noise
Don’t let the Agent read pages of verbose terminal logs.
If a build fails, do not feed the entire log into the chat context. Instead, copy and paste only the specific error message or stack trace. This saves a significant amount of tokens and prevents the LLM from getting lost in the noise.
4. Ignore the Irrelevant
Configure your .cursorignore or settings properly.
Explicitly exclude unnecessary large JSON data and files from the AI’s view. If the AI doesn't need to read it to solve the problem, it shouldn't be in the context window.
Workflow & Interaction
How you talk to the Agent matters as much as what you are asking for.
5. Be Specific with @ Mentions
Don’t make the AI guess.
Explicitly tag relevant files (e.g., "Update @AuthService.ts") to narrow the scope. This creates a focused Retrieval-Augmented Generation (RAG) look-up, preventing hallucinations and ensuring the AI is looking at the right source of truth.
6. Spec/Test First (TDD)
Define “done” before you start.
Adopt a Test-Driven Development approach:
- Ask the Agent to write a failing test based on your requirements.
- Then, ask it to write the code to make that test pass. This provides a clear success metric for the model to aim for.
7. Split Complex Tasks
LLMs degrade as context grows.
Don't try to build a full feature in one long chat session. Separate "Build the API" and "Build the UI" into different chat threads. Keeping the context fresh and focused prevents the model from "forgetting" earlier instructions or getting confused by old code snippets.
Troubleshooting & Strategy
When the AI gets stuck, your strategy needs to change.
8. Reject to Refine
Don’t fix the AI’s bad code yourself.
If you manually fix the generated code, you lose the learning loop within that session. Use the "Reject" feature in Cursor (CMD/Ctrl + N) to force the model to self-correct. Make it explain why the first attempt failed.
9. The “Reset” Strategy
If a bug isn’t fixed after multiple tries, stop.
The context is likely polluted with bad attempts and confusion.
- Stop the current approach.
- Go back to your first issue-fixing prompt.
- Edit the prompt with more specific details or constraints.
- Submit from that previous message (using the "Continue and Revert" logic).
10. Match the Model to the Task
Don't use a sledgehammer to crack a nut.
Using the most powerful model for everything is slow and expensive.
- Boilerplate / Simple fixes: Use faster models (Gemini Flash, Grok Code).
- Complex Architecture / Logic: Use top-tier models (Claude 4.5 Sonnet, Gemini 3 Pro).
Final Thoughts
Efficiency in the age of AI isn’t just about speed—it’s about precision. By managing context and treating tokens as a resource, you get better code for less money.
How are you adapting your workflow to these new tools? Let me know in the comments! 👇
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