Mastering Cursor AI: 5 Pro Tips for Flawless Code Generation

With the rapid advancements in AI, numerous tools like Claude, Cursor, and VS Code with Copilot have become integral to modern development workflows. After using these tools extensively, I've compiled my top five tips for getting the absolute best results out of Cursor. Let's dive in.

Tip 1: Be Hyper-Specific with Your Requirements

This might not be a surprise, but being incredibly specific about what you want to build is the most critical step. Imagine you're a senior developer guiding a junior dev. You wouldn't tell them to just "start coding." You'd expect them to create a detailed, step-by-step plan, ask clarifying questions, and confirm their understanding before writing a single line of code. You should treat Cursor the same way.

Instead of a vague request like "build me a SaaS app," which is full of ambiguity, provide a detailed prompt.

Note: Here is an example of a detailed prompt.

Build me a SaaS app where users can vote on topics they want me to cover in future articles.

A user gets five free votes. After that, they need to pay for a $20/month subscription to get unlimited votes.

Build this application using Next.js. The UI should have a modern, minimalist, dark-mode theme.

The app will need to take payments. Please provide three options for payment processing services that offer subscription payments and a summary of why each would be a good option.

Write a detailed plan for this SaaS app. If you are not 90% sure on how to proceed, ask me some clarifying questions before you begin.

By doing this, you force the AI to think, research, and plan. Instead of jumping into the problem and building something you're not happy with, Cursor will first come up with a detailed plan that you can review and tweak.

As you can see from the prompt, it's better to ask for a plan first. The AI might come back with clarifying questions to ensure it's on the right track.

For instance, it might ask: * Voting Mechanics: How should the voting work? An up-arrow system? * User Management: Do we need full user authentication and profiles? * Content Management: How will new topics be added to the system? * Payment Processor: Based on its research, it might recommend Stripe due to its flexibility, low fees, and excellent integration with Next.js.

You can then answer these questions and ask it to update the plan. The result is a ludicrously long and detailed plan, which is exactly what you want. It might include an overview, technical architecture, folder structure, database schema, UI/UX feel, authentication flow, payment integration, core features, API endpoints, and even development phases.

Tip 2: Document and Save Your Plan

Once you have a comprehensive plan you're happy with, ask Cursor to save it so you can refer back to it later.

Save this detailed plan in a markdown file called scope.md

Now you have a scope.md file containing all the detailed information about the project. You can use this file to continue your session with the LLM, share it with your team, or check it into GitHub so everyone is aligned on the project's goals.

Tip 3: Implement in Small, Incremental Steps

When you start building, begin with the smallest possible piece of the puzzle. Don't ask the AI to implement the entire massive plan at once. If you do, it will have to juggle the Next.js project setup, database schema, authentication, UI, voting system, and payment integration all at the same time.

Instead, focus on one granular task at a time, test it, and then move on.

Let's focus on the first part of phase one. Create a new Next.js app. Only do this one step and then mark it as complete in the scope.md file.

This approach is crucial because LLMs can sometimes "hallucinate" and run an incorrect CLI tool or modify an unrelated part of the codebase. Keeping changes small and isolated makes them easy to review and, if necessary, roll back.

A great workflow is to use Git for version control. Once a small piece of functionality is working correctly (even if the design isn't perfect), commit it.

git add .
git commit -m "Feature: Initial project setup complete"

Think of it like saving your progress in a video game. You create small, incremental save points on your way to a larger goal. If the AI breaks something in the next step—for example, while fixing the UI design—you have a convenient save point to revert to. If you don't like having many small, work-in-progress commits, you can always squash them into a single, more descriptive commit before pushing to a shared repository.

Tip 4: Let the AI Debug for You

You will encounter errors. It's an unavoidable part of development. When you do, simply copy the entire error message, paste it into the chat, and ask Cursor to fix it.

You don't even need to be polite about it. Just paste the ugly error block and write "Fix it."

For even better results, provide more context. You could say, "I was expecting X to happen, but I'm seeing this error instead." Even better, take a screenshot of the application in its broken state, drag and drop it into the chat, and say, "This is what the application currently looks like."

If you get stuck in a loop where Cursor repeatedly tries and fails to fix the issue, try switching to a different model (e.g., from Claude 3 Sonnet to GPT-4o). A fresh perspective can often solve the problem. This is like having an entire team of junior developers collaborating on a robust solution.

Tip 5: Enrich the AI's Context

Finally, give Cursor as much additional context as you can. The more information it has, the better its output will be.

• Provide Documentation: You can give it a link to a documentation page and ask it to build a feature based on it. > "Here is the docs page for the feature I want to build: [link-to-docs]"
• Use Visual References: Take a screenshot of a design you like and say, "I want to build something that looks like this."
• Configure MCP Servers: For advanced use cases, you can set up MCP (Model-Controllable Programming) servers. By creating a cursor/mcp.json file, you can allow the LLM to query its own context about your specific project, such as your Superbase database schema, edge functions, and authorization rules.