Windsurf is an AI coding assistant that generates code based on the context you provide. If you don’t give it enough context, it behaves like a new teammate with no prior knowledge. This matters because better context directly improves code accuracy, consistency, and usefulness. This guide is for developers who want more reliable results from AI-assisted coding.
Key Takeaways
- Windsurf does not retain full context by default, so you must provide it explicitly.
- Rules, Memories, @mentions, and Impersonation are the 4 core ways to guide its behavior.
- Clear and structured context produces better code than long, vague instructions.
What is Windsurf in AI coding?
Windsurf is an AI-powered coding assistant that generates and modifies code based on user prompts and contextual inputs. It relies on the information you provide in each interaction to produce results.
This behavior is aligned with how large language models (LLMs) work: they generate outputs based only on the input context they receive, not long-term memory (OpenAI Prompt Engineering Guide).
Bottom line: Windsurf performs best when you explicitly define what it should know before generating code.
Why does Windsurf need context to generate better code?
Windsurf needs context because it cannot reliably infer your project structure, goals, or constraints on its own.
Without context:
- It may generate incorrect or irrelevant code
- It can modify unintended files
- It may ignore project-specific conventions
Research on generative AI systems shows that models perform better when given explicit instructions and relevant examples (Google Cloud Prompt Design Guide).
With proper context:
- Code aligns with your architecture
- Outputs are more predictable
- You reduce rework and corrections
Conclusion: Context acts as the “memory layer” that makes AI outputs usable in real projects.
How to use Rules in Windsurf?
Rules are predefined instructions that control how Windsurf behaves across conversations or projects. They act as guardrails that reduce randomness and enforce consistency.
Providing structured instructions is a core prompt engineering technique, where clear constraints help guide model outputs toward desired formats and behaviors (OpenAI Prompt Engineering Guide).
Types of Rules
| Type |
Scope |
Example |
| Global Rules |
All projects |
“Write code in English, respond in Spanish.” |
| Project Rules |
Single project |
“Always read the README before answering.” |
How to use Rules effectively (step-by-step)
- Define behavior clearly
Example: “Use TypeScript for all code.” - Set language and formatting preferences
Example: “All comments must be in English.” - Add project-specific instructions
Example: “Follow the structure defined in README.md.” - Keep rules minimal
Too many rules reduce clarity and can confuse the model.
Pro tip (based on practice): In our tests, 3–5 highly specific rules outperform long rule lists, because the model prioritizes clearer signals.
Bottom line: Use fewer, clearer rules to guide consistent outputs.
What are Windsurf Memories and When Should You Use Them?
Memories are stored pieces of context that Windsurf uses to remember important project information over time.
They function similarly to persistent notes about your project.
How Memories work
- Windsurf can auto-generate memories based on conversations
- You can also manually create memories
- They are project-specific (not global)
- You can edit them anytime
Example of a Memory
“This app is a SaaS dashboard for managing subscriptions.”
When to use Memories
Use Memories when:
- You are working on long-term projects
- You want to avoid repeating the same explanations
- Your project has stable requirements
- You need consistent context across sessions
Avoid overusing Memories when:
- You want exploratory or creative outputs
- Your project changes frequently
Important limitation: Memories can become outdated if your project evolves, so you must review and update them regularly.
Conclusion: Memories reduce repetition but require maintenance to stay accurate.
How to Use @mentions in Windsurf to Provide Context?
@mentions allow you to reference specific files, code, or documentation directly in your prompt.
This reflects a key prompt engineering principle: providing grounded context (real data or documents) reduces hallucinations and improves accuracy (OpenAI Prompt Engineering Guide).
Examples of @mentions
@README → Loads project overview@server.js → References backend logic@/components/Button.tsx → Targets a specific UI file
How to use @mentions (step-by-step)
- Reference the exact file or resource
- Give a clear instruction
Example: “Read @README and summarize the architecture.” - Limit scope
Prevents Windsurf from modifying unrelated files
Why this works:
You eliminate guesswork by forcing the model to use real project data instead of assumptions.
Bottom line: @mentions are the fastest way to inject precise, relevant context.
What is "Impersonation" in Windsurf and How Does it Work?
Impersonation is a technique where Windsurf adopts a specific role or persona to guide its outputs.
This is similar to role-based prompting, a widely used technique where assigning a role improves output relevance and tone (OpenAI Prompt Engineering Guide).
This is useful for tasks that require a particular perspective, such as design, QA, or architecture.
Example of impersonation
“Impersonate a senior UX designer focused on usability.”
Advanced use: Persona files
You can create reusable profiles (e.g., @luna.md) that define:
- Tone
- Priorities
- Constraints
Then use: “Impersonate @luna”
Use cases for Impersonation
- UX/UI design perspectives
- Code review roles
- Architecture decision-making
- Testing and QA validation
Why it works
Impersonation narrows the model’s decision space by:
- Defining priorities (e.g., usability vs performance)
- Applying consistent criteria across outputs
Real-world workflow tip: In practice, teams use impersonation to:
- Generate quick prototypes (HTML/CSS)
- Validate ideas before implementation
- Run AI-powered code reviews after development
Conclusion: Impersonation adds focus and expertise to AI outputs.
Rules vs Memories vs @mentions vs Impersonation
| Feature |
Purpose |
Scope |
Best Use Case |
| Rules |
Define behavior |
Global / Project |
Consistency |
| Memories |
Store context |
Project |
Long-term projects |
| @mentions |
Inject data |
Instant |
Precision |
| Impersonation |
Change perspective |
Task-based |
Specialized outputs |
How to give Windsurf the best context (Checklist)
Use this checklist before prompting:
- ✅ Define clear rules
- ✅ Add key memories
- ✅ Reference files with @mentions
- ✅ Use impersonation for complex tasks
- ✅ Keep instructions short and specific
Common mistakes when using Windsurf
- ❌ Giving too many rules at once
- ❌ Not updating memories after changes
- ❌ Writing vague prompts
- ❌ Not referencing actual files
- ❌ Expecting Windsurf to “just know” your project
Fix: Always provide explicit, structured context.
Final Summary
To get better results from Windsurf, you need to control its context.
Use rules for consistency, memories for persistence, @mentions for precision, and impersonation for focus.
The clearer your context, the better your code.
Windsurf is an AI coding assistant that generates code based on the context you provide. If you don’t give it enough context, it behaves like a new teammate with no prior knowledge. This matters because better context directly improves code accuracy, consistency, and usefulness. This guide is for developers who want more reliable results from AI-assisted coding.
Key Takeaways
- Windsurf does not retain full context by default, so you must provide it explicitly.
- Rules, Memories, @mentions, and Impersonation are the 4 core ways to guide its behavior.
- Clear and structured context produces better code than long, vague instructions.
What is Windsurf in AI coding?
Windsurf is an AI-powered coding assistant that generates and modifies code based on user prompts and contextual inputs. It relies on the information you provide in each interaction to produce results.
This behavior is aligned with how large language models (LLMs) work: they generate outputs based only on the input context they receive, not long-term memory (OpenAI Prompt Engineering Guide).
Bottom line: Windsurf performs best when you explicitly define what it should know before generating code.
Why does Windsurf need context to generate better code?
Windsurf needs context because it cannot reliably infer your project structure, goals, or constraints on its own.
Without context:
- It may generate incorrect or irrelevant code
- It can modify unintended files
- It may ignore project-specific conventions
Research on generative AI systems shows that models perform better when given explicit instructions and relevant examples (Google Cloud Prompt Design Guide).
With proper context:
- Code aligns with your architecture
- Outputs are more predictable
- You reduce rework and corrections
Conclusion: Context acts as the “memory layer” that makes AI outputs usable in real projects.
How to use Rules in Windsurf?
Rules are predefined instructions that control how Windsurf behaves across conversations or projects. They act as guardrails that reduce randomness and enforce consistency.
Providing structured instructions is a core prompt engineering technique, where clear constraints help guide model outputs toward desired formats and behaviors (OpenAI Prompt Engineering Guide).
Types of Rules
| Type |
Scope |
Example |
| Global Rules |
All projects |
“Write code in English, respond in Spanish.” |
| Project Rules |
Single project |
“Always read the README before answering.” |
How to use Rules effectively (step-by-step)
- Define behavior clearly
Example: “Use TypeScript for all code.” - Set language and formatting preferences
Example: “All comments must be in English.” - Add project-specific instructions
Example: “Follow the structure defined in README.md.” - Keep rules minimal
Too many rules reduce clarity and can confuse the model.
Pro tip (based on practice): In our tests, 3–5 highly specific rules outperform long rule lists, because the model prioritizes clearer signals.
Bottom line: Use fewer, clearer rules to guide consistent outputs.
What are Windsurf Memories and When Should You Use Them?
Memories are stored pieces of context that Windsurf uses to remember important project information over time.
They function similarly to persistent notes about your project.
How Memories work
- Windsurf can auto-generate memories based on conversations
- You can also manually create memories
- They are project-specific (not global)
- You can edit them anytime
Example of a Memory
“This app is a SaaS dashboard for managing subscriptions.”
When to use Memories
Use Memories when:
- You are working on long-term projects
- You want to avoid repeating the same explanations
- Your project has stable requirements
- You need consistent context across sessions
Avoid overusing Memories when:
- You want exploratory or creative outputs
- Your project changes frequently
Important limitation: Memories can become outdated if your project evolves, so you must review and update them regularly.
Conclusion: Memories reduce repetition but require maintenance to stay accurate.
How to Use @mentions in Windsurf to Provide Context?
@mentions allow you to reference specific files, code, or documentation directly in your prompt.
This reflects a key prompt engineering principle: providing grounded context (real data or documents) reduces hallucinations and improves accuracy (OpenAI Prompt Engineering Guide).
Examples of @mentions
@README → Loads project overview@server.js → References backend logic@/components/Button.tsx → Targets a specific UI file
How to use @mentions (step-by-step)
- Reference the exact file or resource
- Give a clear instruction
Example: “Read @README and summarize the architecture.” - Limit scope
Prevents Windsurf from modifying unrelated files
Why this works:
You eliminate guesswork by forcing the model to use real project data instead of assumptions.
Bottom line: @mentions are the fastest way to inject precise, relevant context.
What is "Impersonation" in Windsurf and How Does it Work?
Impersonation is a technique where Windsurf adopts a specific role or persona to guide its outputs.
This is similar to role-based prompting, a widely used technique where assigning a role improves output relevance and tone (OpenAI Prompt Engineering Guide).
This is useful for tasks that require a particular perspective, such as design, QA, or architecture.
Example of impersonation
“Impersonate a senior UX designer focused on usability.”
Advanced use: Persona files
You can create reusable profiles (e.g., @luna.md) that define:
- Tone
- Priorities
- Constraints
Then use: “Impersonate @luna”
Use cases for Impersonation
- UX/UI design perspectives
- Code review roles
- Architecture decision-making
- Testing and QA validation
Why it works
Impersonation narrows the model’s decision space by:
- Defining priorities (e.g., usability vs performance)
- Applying consistent criteria across outputs
Real-world workflow tip: In practice, teams use impersonation to:
- Generate quick prototypes (HTML/CSS)
- Validate ideas before implementation
- Run AI-powered code reviews after development
Conclusion: Impersonation adds focus and expertise to AI outputs.
Rules vs Memories vs @mentions vs Impersonation
| Feature |
Purpose |
Scope |
Best Use Case |
| Rules |
Define behavior |
Global / Project |
Consistency |
| Memories |
Store context |
Project |
Long-term projects |
| @mentions |
Inject data |
Instant |
Precision |
| Impersonation |
Change perspective |
Task-based |
Specialized outputs |
How to give Windsurf the best context (Checklist)
Use this checklist before prompting:
- ✅ Define clear rules
- ✅ Add key memories
- ✅ Reference files with @mentions
- ✅ Use impersonation for complex tasks
- ✅ Keep instructions short and specific
Common mistakes when using Windsurf
- ❌ Giving too many rules at once
- ❌ Not updating memories after changes
- ❌ Writing vague prompts
- ❌ Not referencing actual files
- ❌ Expecting Windsurf to “just know” your project
Fix: Always provide explicit, structured context.
Final Summary
To get better results from Windsurf, you need to control its context.
Use rules for consistency, memories for persistence, @mentions for precision, and impersonation for focus.
The clearer your context, the better your code.
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