We recently stumbled on a thread from Windsurf asking the community:

Developers jumped in fast. They shared tighter workflows, better debug loops, faster build strategies, and ways to make an AI-powered editor like Windsurf feel less like a chatbot and more like a coding partner.

If you followed closely, you've likely picked up a handful of sharp ideas. We've pulled the best ones, tested them inside projects, and trimmed them down to the tips that work across Windsurf AI and Cursor AI.

Here are six practical tips you can start using today to make these two outstanding AI coding tools work better for you. They are quick to learn, easy to apply, and written for developers who would rather ship code than scroll through endless replies.

TL;DR

• Keep each thread focused on a single task.
• Pull docs inline with /docs to cut context switching.
• Let the agent handle your build loop.
• Reference full files instead of chat snippets.
• Prompt like a product spec.
• Reset messy sessions with a stand-up-style prompt.

Now, open your editor, and let’s practice together.

1. Keep threads focused — one task per session

Both Cursor and Windsurf perform best when interactions are kept short and targeted. Rather than letting threads spiral into long, rambling conversations that mix multiple topics, start a fresh thread for every distinct task. This practice minimizes cascade calls and context drift, reducing the risk that AI will hallucinate details or miss important contexts.

For example, imagine you have a long thread where you begin by asking for help debugging a code snippet. Midway through the conversation, you pivot to asking about integrating a new library and later add a query regarding UI improvements or deployment best practices, all within the same session. This approach overloads the context window, making it difficult for the AI to track which details belong to which task.

To avoid this, treat every task as its own conversation. Start by opening a thread solely dedicated to debugging your code snippet. Once that task is complete, start a new thread when you’re ready to discuss integrating a new library. This approach keeps interactions focused, conserves tokens, and saves mental energy.

Task 1: Implementing a new feature

Task 2: Refactoring code

2. Query docs inline using @docs

Cursor and Windsurf both include a @docs:<source> command so you can fetch precise documentation snippets without ever leaving your editor. By prepending your query with /docs: , the agent reaches into your indexed docs and returns only the relevant section; no more switching tabs or pasting links into your prompt.

This shortcut works best when your documentation is published with an /llms.txt index file, which doc tools like Mintlify provide out of the box. Using Mintlify or any provider that hosts all docs with /llms.txt, you give Cursor and Windsurf a clear roadmap to your docs.

Beyond the convenience, inline docs greatly improve your interactions. Grounding the model in your reference materials removes the chances of hallucinations, and because you only pull in the tiny snippet you need, you save on token usage. The result is faster, and you get more accurate answers in your workflow.

3. Let the AI code editor handle the build loop

Cursor's experimental YOLO/Auto-Run mode and Windsurf's terminal integration let your agent execute shell commands like npm install or tsc and automatically re-run them until the build succeeds. This hands-off approach lets you focus on higher-value logic and architectural decisions, freeing you from repetitive debugging tasks, such as persistent type-checking failures or dependency installations.

While auto-executed commands can speed up your workflow, allowing your AI agents to run shell commands can introduce serious security risks if left unchecked. It’s best to enable auto‑execution only on trusted code bases and within clearly scoped directories or scripts; Windsurf's Auto-executed Cascade commands feature runs safe commands automatically, while risky commands require user permission. That way, you avoid the chance of a misconfigured command making unintended changes.

• Auto will auto-execute commands.
• Turbo will always execute the command, unless it is on the deny list.

4. Reference files, not chat snippets

Instead of copying and pasting code snippets into the chat window, leverage your tool's ability to reference entire files or buffers. In Cursor, you can use the /Reference Open Editors command to include all currently open editor tabs in the AI's context. This approach provides the AI with a comprehensive view of your codebase, enabling it to understand the entire structure and flow, which helps maintain the context of functions and variables.

By referencing files directly, you reduce token usage, as the AI processes the entire file context without requiring repetitive or redundant inputs. This not only makes the interaction seamless but also guarantees that the AI maintains the correct scope of variables and functions, leading to more accurate and relevant suggestions.

To maximize the effectiveness of this method, consider these community-suggested practices:

• Open only relevant files: Keep your editor focused by closing unnecessary tabs. This ensures the AI's context is limited to pertinent code, reducing the chance of confusion or irrelevant suggestions.
• Maintain clean, modular files: Smaller, well-organized files make it easier for the AI to process and understand your code, leading to more accurate and efficient responses.

5. Prompt like a product spec

Vague instructions, such as “Improve performance,” often result in back-and-forth clarifications or generic advice. Instead, structure your prompt in a concise way that covers exactly what you’re building, what’s in place already, and any boundaries or preferences. This level of detail enables the AI to highlight the right solution in a single pass.

Do this instead:

• Begin with what you’re trying to build: Clearly state the feature or component you want the AI to produce or modify. For example, “I need a function that fetches user profiles from /api/users and renders them in a React list.”
• Next, describe what exists already: Point the AI at the current code, identifying relevant files or snippets. For instance, “In UserList.tsx, you already have a useEffect hook that calls /api/users but it doesn’t handle loading states.”
• Finally, specify any constraints, edge cases, or preferences such as a performance target “cache results for 30 seconds”, a library restriction “use only the built‑in Fetch API”, a style guideline “follow our team’s Tailwind classes”, and potential edge cases like empty responses, HTTP errors, or authentication failures.

To speed up that handoff even further, here are some templates you could try:

Goal: [Brief description of the feature].  

Existing: [File(s) or function(s) already written].  

Constraints: [Performance, library, style, browser compatibility].  

Edge Cases: [Empty data, errors, null values].  

Preferences: [Coding style, file structure].

I’m building: [Feature X].  

Current code: [Reference Open Editors → File Y].  

Require:

- [List constraint or requirement 1]

- [List constraint or requirement 2]  

Handle: [Edge case 1, Edge case 2].  

Use: [Preferred patterns or libraries].

Example in action

Goal: Enhance the `BookListView` to show loading and error states.

Existing: In `views.py`, i fetch books but only render once data arrives.

Constraints: Use Django's built-in functionality only, no extra packages; show loading state using Django's messages framework.

Edge Cases: Empty book lists should display "No books found"; database errors show "Failed to load books."

Preferences: Keep styling with Bootstrap classes and maintain consistent error message formatting.

6. Reboot messy sessions using the standup format

Even with precise prompts and inline docs, your AI assistant can still get stuck on old context. If your thread drifts, mixing errors or commands, reset using a clear structure like the standup-style input:

• Goal: What you’re trying to accomplish.
• Attempts: What you’ve already tried.
• Roadblocks: What failed or didn't work as expected

Here's an example of a standup-style input:

Goal: Debug the failed database migration after updating Django version.

Attempts: re-ran migrations.

Roadblocks: Still getting "django.db.utils.OperationalError: no such table: reviews_review" in manage.py.

The above approach clears stale context, reduces token waste, and sharpens the AI's next answer. Windsurf editor performs best in short, focused sessions, and Cursor delivers better results after periodic resets—so you remain in control, not the agent.

Final thoughts

Consistent application of these six habits—keeping threads focused, querying docs inline, automating build loops, referencing full files, prompting like a product spec, and rebooting sessions using the standup format helps turns AI-powered code editors like Cursor and Windsurf into reliable coding partners.

These six tips will make your workflows faster, clearer, and easier to scale. As a bonus, if you're hosting your documentation on Mintlify, you'll unlock features like /docs that make the tools even more efficient.

Pick one habit and try it this week. Repeat it until it becomes second nature. Over time, these habits will compound and eventually transform your workflow.

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