Starting with Cursor can feel like approaching a new mountain peak—exciting but potentially overwhelming. After helping many new users make their first ascent into AI-powered coding, I’ve identified the key strategies that separate successful expeditions from frustrating false starts.

Here are five essential tips to help you navigate your early days with Cursor and make the most of this powerful development environment.

1. Use Your Voice: Be Verbose, Not Succinct

The first and most transformative tip is simple: talk to Cursor instead of typing. This isn’t just about convenience—it fundamentally changes how you communicate with AI.

When we type, we naturally try to be concise. We’ve been trained by years of messaging and code comments to keep things brief. But large language models thrive on detail and context. They perform better when you’re verbose, repetitive, and thorough in your explanations.

With voice, repetition becomes natural and beneficial. You can say the same thing multiple ways, add context as you think of it, and provide the kind of detail that helps the AI understand not just what you want, but why you want it.

2. Master Context: Bringing the Right Information at the Right Time

Context is everything in AI-assisted development. Think of it as your base camp—the collection of information that informs every decision and suggestion the AI makes.

In tools like ChatGPT, context is visually obvious: it’s the entire conversation thread, including your messages, the AI’s responses, and any files or images you’ve shared. In Cursor, context works similarly but with powerful additions specific to coding.

Building Effective Context

Select and Include Relevant Code: Use Cmd+K to add specific code selections to your chat context. Don’t just include the function you’re working on—include related functions, type definitions, and imports that might be relevant.

Reference Related Files: When working on a component, include its parent components, any shared utilities it uses, and related test files. This helps the AI understand the broader architecture and coding patterns.

Explain Your Architecture: Don’t assume the AI knows your project structure. Briefly explain how your codebase is organized, what frameworks you’re using, and any specific patterns or conventions your team follows.

The more thoughtful you are about context, the more accurate and helpful the AI’s suggestions become.

3. Understand Model Capabilities: Choose the Right Tool for the Job

One of the biggest mistakes new users make is using weak models for complex tasks or powerful models for simple ones. It’s like trying to summit Everest with basic hiking gear—you need the right equipment for the challenge.

Matching Models to Tasks

For code generation and complex reasoning, you want the most capable models available:

• Claude 3.5 Sonnet/4.0 - Excellent for architecture decisions and complex refactoring
• GPT-4 or GPT-4 Turbo - Strong general coding abilities
• Gemini Pro - Good balance of capability and speed

For simple completions and quick edits, lighter models work fine and respond faster:

• GPT-3.5 Turbo - Fast for basic completions
• Claude 3 Haiku - Quick responses for simple tasks

When to Use Which Model

Think of model capabilities like olive oil—always buy the best one you can afford for the tasks that matter most.

Cost vs. Quality Trade-offs

Model selection often comes down to cost considerations. The most capable models like Claude 3 Opus are powerful but expensive, while Claude 3.5 Sonnet offers an optimized balance of cost and quality that makes it excellent for most coding tasks.

For different scenarios:

Use premium models (Claude 3 Opus, GPT-4, O1) when:

• Working with ambiguous requirements that need deep reasoning
• Synthesizing plans from multiple documentation sources
• Handling complex multi-file operations where reliability is crucial

Use cost-optimized models (Claude 3.5 Sonnet, Gemini 2.5 Pro) when:

• You can provide specific context and clear requirements
• Working on well-defined tasks with good examples
• Building features where you can iterate and refine

Use efficient models (GPT-3.5 Turbo, Claude 3 Haiku) for:

• Simple code rewrites and refactoring
• Basic completions and syntax fixes
• Quick documentation tasks

The key is matching the model’s reasoning power to the complexity and ambiguity of your task. Clear context and specific asks can often allow you to use a more cost-effective model successfully.

4. Plan Your Work: From Sketches to Implementation

Planning in the AI development era requires a different approach than traditional development. The ability to rapidly prototype and iterate means you can work at lower fidelity initially and let the models help you discover the best path forward.

Visual and Audio Planning

Start with rough sketches: Use tools like FigJam to visually map out features without getting caught up in high-fidelity mockups. The goal is understanding the problem space, not pixel-perfect designs.

Talk through your ideas: Record yourself explaining the feature in detail. This audio becomes valuable input for AI models that can process multimodal content.

Create planning artifacts: Use tools like Gemini 2.5 Pro to convert your audio recordings and documentation into structured markdown plans that can guide implementation.

The Converge-Diverge Workflow

This planning approach follows a familiar UX pattern:

1. Diverge: Explore ideas through drawing, talking, and gathering feedback from real users
2. Converge: Synthesize insights into a clear text artifact (your implementation plan)
3. Diverge: Let the AI agent implement the work based on your plan
4. Converge: Verify, test, and refine the implementation

AI can assist at every stage of this workflow—from synthesizing audio notes into plans to implementing features and providing feedback on the results.

5. Go Slow to Go Fast: Avoiding Assumption Stacks

One of the biggest pitfalls in AI-assisted development is moving too quickly and building assumptions on top of assumptions. When you rely on AI for implementation details, it’s easy to lose alignment between what you think you’ve built and what actually exists.

The Assumption Stack Problem

When things unravel in AI development, it’s often because you’ve accumulated multiple layers of unverified assumptions:

• Assuming the AI correctly understood your requirements
• Assuming the implementation works as described
• Assuming edge cases are handled properly
• Assuming the feature integrates well with existing code

Strategies for Staying Aligned

Work in smaller loops: Break features into minimal testable increments. Verify each piece works before building on top of it.

Build verification tools: Encourage the AI to create test harnesses, logging systems, or demo pages that help you validate what’s being built.

Use rich context for verification: Leverage screenshots from test environments, log outputs, and MCP servers to give the AI current state information for debugging.

Ask for breakdown and verification plans: When starting a complex feature, ask the AI to break it into smaller steps and suggest verification methods for each step.

Think of this like working with any stakeholder—going too fast leads to misalignment, which creates frustration for everyone involved. The AI can actually help you slow down by breaking complex tasks into manageable pieces and suggesting verification strategies.

Your Next Steps on the Cursor Trail

Cursor represents a fundamental shift in how we write code—from solitary problem-solving to collaborative development with AI. Like any new tool, it requires patience and practice to master.

Start with these five principles, but remember that the best way to learn Cursor is to use it on real projects. Choose a small feature or bug fix, apply these techniques, and pay attention to what works best for your specific needs and projects.

The landscape of AI-assisted development is evolving rapidly, but these fundamentals—clear communication, thoughtful context, appropriate tool selection, careful planning, and measured pacing—will serve you well regardless of how the technology advances.

Happy coding, and may your AI-assisted development journey be both productive and enjoyable.

Resources for Your AI Journey

For more insights on AI-powered development and practical tools to enhance your workflow, explore Aqua—a comprehensive platform designed to help you navigate the evolving landscape of AI-assisted development.