From Prompt to Prototype in Minutes: Code with Windsurf Editor
Published on
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February 23, 2026
Last updated on
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February 16, 2026
Time to read
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12
Valentina Ibinete
Marketing Lead
If you're curious about where AI is headed in software development, it's time to pay attention to Windsurf: an IDE powered by artificial intelligence that feels less like a tool and more like a copilot.
Before you jump into coding, take a step back and plan. This is where ChatGPT (or another conversational model) can shine. Use it to brainstorm ideas, organize your thoughts, and shape your prompt.
Let’s say you want to build a chatbot. You can ask ChatGPT to help you structure a well-defined prompt tailored for Windsurf.
🔍 Pro tip: The more complex or abstract your prompt is, the higher the chance of messy or overly generic code. If long-term maintainability matters, spend time writing a thoughtful prompt.
Step 2: Choose the Right Model
On the right side of the Windsurf interface, you’ll find Cascade, your AI copilot. Cascade can tap into several powerful foundation models:
Each of these models brings its own strengths, and you can choose which one to use based on your goals or the task complexity.
Some models, like Sonnet 3.7 (Thinking), use internal reasoning steps before generating final output. You’ll see the model “think out loud,” iterating on your prompt in real time. These “thinking” models are more resource-intensive and slightly slower, but they’re better at solving complex problems.
However, they can sometimes overcomplicate things or drift off-track. A helpful strategy is to use them for ideation and problem-solving, then switch to a faster, more direct model for implementation.
🧠 Is one model better than the others? Sometimes. But results are non-deterministic. The best way to find what works is to experiment with models and prompt variations.
Step 3: Give Windsurf the Right Context
Windsurf starts every session with a clean slate. This means it doesn’t remember anything you’ve said in past conversations.
Just like you would explain things to a new teammate, you’ll need to provide all the relevant information upfront before asking it to write code. For example:
If you're building a chatbot, you might begin by asking Windsurf to read a README file so it understands the system’s specs and expected behavior.
Context is everything. Without it, Windsurf can’t generate helpful code. Here’s how to provide the right context:
Upload or paste documentation (README, API references, etc.)
Pro tip, ask Windsurf to generate the README file and documentation in MD format, and then edit it accordingly
Use features like rules and memories to keep recurring information handy
⚠️ Reminder: Windsurf doesn’t retain information between sessions unless you explicitly use memory or rule features. Be intentional and include all necessary context up front.
Want to go deeper? Check out our dedicated blog post on giving Windsurf proper context.
Step 4: Paste Your Prompt
Once you’ve set the context, paste your prompt into the same conversation thread. From here, Windsurf takes over, picking the right tools, building your code step by step, and shows you its thinking along the way.
Before making any changes to the code, always present the plan and ask for confirmation.” That way, we can review it, and models don’t go ahead and make changes on their own. So that’s a rule we should always stick to.
You can ask for edits, improvements, or further explanations as you go.
Step 5: Run Your App on Localhost
One of Windsurf’s most powerful features is its ability to generate full-stack applications (frontend and backend) in just minutes. It can:
Configure your environment
Generate working code
Run it locally using the “One Shot” flow
One Shot = one solid prompt → full app prototype
This is fantastic for prototyping and fast iteration. But it’s not a replacement for good software engineering practices.
💡 Important: One Shot outputs aren’t production-ready. Always review the code. AI can take you far, but great software still needs great developers behind it.
Step 6: Make Changes & Refine
Windsurf extends beyond the chat interface by offering intelligent autocompletion within the code editor, providing contextually relevant suggestions as you type.
Plus, there's this cool shortcut: Command + L. By selecting a portion of code and using this command, you can then enter instructions such as "comment this function," and Windsurf will propose the corresponding modification.
For every change Windsurf suggests, a little window will show up where you can check out the proposed code edits and either click "Accept" or "Decline".
Now, here's the deal: If you don't like what Windsurf makes and you just keep telling it to change stuff over and over, it can start adding a bunch of extra, messy code. It will keep adding new styles without getting rid of the old ones. It can get into this weird loop where it just keeps piling on code, and you won't even know what's being used and what's just junk.
Think about it this way: when you come back to your project tomorrow, Windsurf won't remember any of your previous chats. So, it'll use its search tool, find some old style that's not even being used, and try to change that instead. Then, nothing works, and you're wondering what's going on.
Remember, Windsurf's memory is short. It's like saying "hi" and having to introduce yourself again the next time. That's why writing clean, organized code makes it way easier for Windsurf's search tools to do their job right.
So, one of the best ways to handle this is to go back a step. You can literally tell Windsurf, "Forget that," using the "truncate conversation" feature. It's pretty sweet because you can hit "Revert this step," confirm it, and Windsurf will not only forget what it just did but also undo the code changes.
Bottom line: It's usually better to tweak your original prompt and go back than to keep asking Windsurf to fix something you didn't like in the first place.
Wrap Up
Start small. Test different models. And most importantly, don’t skip the human touch. Even with powerful AI, great software still depends on great developers.
👉 Next up: We'll walk through the best ways to give Windsurf context so you can get even more of its potential.
If you're curious about where AI is headed in software development, it's time to pay attention to Windsurf: an IDE powered by artificial intelligence that feels less like a tool and more like a copilot.
Before you jump into coding, take a step back and plan. This is where ChatGPT (or another conversational model) can shine. Use it to brainstorm ideas, organize your thoughts, and shape your prompt.
Let’s say you want to build a chatbot. You can ask ChatGPT to help you structure a well-defined prompt tailored for Windsurf.
🔍 Pro tip: The more complex or abstract your prompt is, the higher the chance of messy or overly generic code. If long-term maintainability matters, spend time writing a thoughtful prompt.
Step 2: Choose the Right Model
On the right side of the Windsurf interface, you’ll find Cascade, your AI copilot. Cascade can tap into several powerful foundation models:
Each of these models brings its own strengths, and you can choose which one to use based on your goals or the task complexity.
Some models, like Sonnet 3.7 (Thinking), use internal reasoning steps before generating final output. You’ll see the model “think out loud,” iterating on your prompt in real time. These “thinking” models are more resource-intensive and slightly slower, but they’re better at solving complex problems.
However, they can sometimes overcomplicate things or drift off-track. A helpful strategy is to use them for ideation and problem-solving, then switch to a faster, more direct model for implementation.
🧠 Is one model better than the others? Sometimes. But results are non-deterministic. The best way to find what works is to experiment with models and prompt variations.
Step 3: Give Windsurf the Right Context
Windsurf starts every session with a clean slate. This means it doesn’t remember anything you’ve said in past conversations.
Just like you would explain things to a new teammate, you’ll need to provide all the relevant information upfront before asking it to write code. For example:
If you're building a chatbot, you might begin by asking Windsurf to read a README file so it understands the system’s specs and expected behavior.
Context is everything. Without it, Windsurf can’t generate helpful code. Here’s how to provide the right context:
Upload or paste documentation (README, API references, etc.)
Pro tip, ask Windsurf to generate the README file and documentation in MD format, and then edit it accordingly
Use features like rules and memories to keep recurring information handy
⚠️ Reminder: Windsurf doesn’t retain information between sessions unless you explicitly use memory or rule features. Be intentional and include all necessary context up front.
Want to go deeper? Check out our dedicated blog post on giving Windsurf proper context.
Step 4: Paste Your Prompt
Once you’ve set the context, paste your prompt into the same conversation thread. From here, Windsurf takes over, picking the right tools, building your code step by step, and shows you its thinking along the way.
Before making any changes to the code, always present the plan and ask for confirmation.” That way, we can review it, and models don’t go ahead and make changes on their own. So that’s a rule we should always stick to.
You can ask for edits, improvements, or further explanations as you go.
Step 5: Run Your App on Localhost
One of Windsurf’s most powerful features is its ability to generate full-stack applications (frontend and backend) in just minutes. It can:
Configure your environment
Generate working code
Run it locally using the “One Shot” flow
One Shot = one solid prompt → full app prototype
This is fantastic for prototyping and fast iteration. But it’s not a replacement for good software engineering practices.
💡 Important: One Shot outputs aren’t production-ready. Always review the code. AI can take you far, but great software still needs great developers behind it.
Step 6: Make Changes & Refine
Windsurf extends beyond the chat interface by offering intelligent autocompletion within the code editor, providing contextually relevant suggestions as you type.
Plus, there's this cool shortcut: Command + L. By selecting a portion of code and using this command, you can then enter instructions such as "comment this function," and Windsurf will propose the corresponding modification.
For every change Windsurf suggests, a little window will show up where you can check out the proposed code edits and either click "Accept" or "Decline".
Now, here's the deal: If you don't like what Windsurf makes and you just keep telling it to change stuff over and over, it can start adding a bunch of extra, messy code. It will keep adding new styles without getting rid of the old ones. It can get into this weird loop where it just keeps piling on code, and you won't even know what's being used and what's just junk.
Think about it this way: when you come back to your project tomorrow, Windsurf won't remember any of your previous chats. So, it'll use its search tool, find some old style that's not even being used, and try to change that instead. Then, nothing works, and you're wondering what's going on.
Remember, Windsurf's memory is short. It's like saying "hi" and having to introduce yourself again the next time. That's why writing clean, organized code makes it way easier for Windsurf's search tools to do their job right.
So, one of the best ways to handle this is to go back a step. You can literally tell Windsurf, "Forget that," using the "truncate conversation" feature. It's pretty sweet because you can hit "Revert this step," confirm it, and Windsurf will not only forget what it just did but also undo the code changes.
Bottom line: It's usually better to tweak your original prompt and go back than to keep asking Windsurf to fix something you didn't like in the first place.
Wrap Up
Start small. Test different models. And most importantly, don’t skip the human touch. Even with powerful AI, great software still depends on great developers.
👉 Next up: We'll walk through the best ways to give Windsurf context so you can get even more of its potential.
Applying changes across microservices is difficult because business logic is distributed across multiple services, each with its own data, contracts, and responsibilities.
In our experiment at Kaizen Softworks, we tested whether an AI system could safely apply coordinated changes across a microservices architecture using only minimal input.
Short answer: Yes, but only when the AI has enough architectural context.
Why are coordinated changes in microservices so hard?
In distributed systems, a single business change rarely affects just one service.
It often requires:
Updating multiple microservices
Modifying message contracts
Keeping DTOs (Data Transfer Objects) consistent
Respecting domain boundaries defined by Domain-Driven Design (DDD)
Key entities in this system:
Microservice: An independently deployable service responsible for a specific domain
Aggregate (DDD): A cluster of domain objects treated as a single unit
DTO (Data Transfer Object): A structured format used to transfer data between services
Message/Event: A communication mechanism between services
The complexity is not in the code, it’s in the relationships between components.
The experiment: Can AI reason across services with minimal input?
We designed a controlled experiment to test whether an AI model could apply system-wide changes with limited information.
Input given to the AI:
Message definitions (events between services)
DTOs (data contracts)
Tasks the AI had to perform:
Identify affected aggregates
Determine service ownership
Apply coordinated changes across services
Maintain consistency in messages and DTOs
In other words, the AI had to behave like a software architect, not just a code generator.
What was the biggest obstacle?
The biggest challenge was not technical, it was contextual.
Problem: unclear service naming
Instead of descriptive names like:
order-service
billing-service
Our services were named:
john
sally
roger
This removed any semantic clues about responsibility.
Result: The AI could not infer which service owned which domain logic.
The missing piece: aggregate ownership mapping
To solve this, we introduced a simple but powerful structure:
Aggregate → Service mapping
Order → john
Shipment → sally
Invoice → roger
This created a clear relationship between domain concepts and system components.
Once ownership was explicit, the architecture became understandable.
How we used AI to generate architectural context
Instead of building this mapping manually, we used AI to analyze the codebase and extract:
Where each aggregate was defined
Which microservice implemented it
The relationship between domain and infrastructure
The result was a machine-readable architecture map.
In practice, we used AI to generate the context that AI itself needed.
Results: Can AI safely apply distributed changes?
With the architecture map in place, the AI was able to:
Trace message flows across services
Identify affected aggregates
Locate the correct microservices
Apply coordinated updates
Maintain consistency between DTOs and messages
While not perfect, the system worked reliably as a proof of concept.
What is the real limitation of AI in microservices?
The main limitation of AI is not code generation, it’s architectural understanding.
Without knowing:
Which components exist
How they relate
Who owns what
AI cannot safely modify a distributed system.
AI performance depends more on context quality than model capability.
When can AI safely modify microservices?
AI works well when:
Aggregate ownership is clearly defined
Message contracts are explicit
Architecture is structured and consistent
AI struggles when:
Naming is ambiguous
Relationships are implicit
Context is incomplete
Simple rule: If the architecture is clear, AI can reason. If not, it guesses.
Final thoughts
This experiment revealed something important:
AI doesn’t fail because it can’t write code. It fails because it can’t see the system.
As teams move toward AI-assisted development, the focus will likely shift from:
Writing better code to Designing better systems for machines to understand
At Kaizen Softworks, we see this as a foundational shift.
Because when AI can understand architecture, it doesn’t just generate code, it helps evolve systems.
There's a myth that in flat organizations, everyone decides on everything.
That's not how it works. At least not at Kaizen.
When people hear "no managers," they often picture one of two extremes: either total chaos where nobody is accountable, or endless meetings where 80 people vote on which coffee to buy. The reality is neither.
Not everyone decides on everything. Not everyone votes. What we do have is a clear set of decision-making methods that we choose based on context.
It depends on who's affected and how deep the impact goes
Before choosing how to decide, we ask ourselves a few questions:
Who is affected? A decision that only impacts one team doesn't need the whole company involved. A decision that affects everyone's daily work does.
How deep is the impact? Changing the office furniture is wide but shallow. Changing the salary model is deep and lasting.
Is it reversible? If we can easily undo it, we can move fast and just inform. If it's hard to reverse, we slow down and include more people.
How urgent is it? And here we're careful to distinguish real urgency from anxiety, the pressure to decide quickly because someone already has "the answer" in mind.
These dimensions help us pick the right method. Not every decision deserves the same process.
Our decision-making toolkit
Over the years, we've landed on a few methods that we use depending on the situation:
1. Role-based decisions
Some decisions belong to a specific role. If someone owns a responsibility, say, office logistics or hiring for a team, they decide within that domain. No committee needed. The key is that roles are transparent: everyone knows who owns what, and the scope of each role's authority is clear.
2. Advice Process
When a decision doesn't clearly belong to one role, or when it crosses boundaries, we use the advice process. Here's how it works:
Someone takes the initiative. They identify the problem and own the process.
They gather input from people who are affected and people with expertise.
They seek advice, real conversations, not rubber-stamping.
They make the decision and communicate it, including what advice they incorporated and what they didn't (and why).
The decision-maker is not a committee. It's one person (or a small group) who takes responsibility. But they don't decide in isolation, they bring in the perspectives that matter.
We sometimes call this "Team Advice" when a working group forms around an issue that doesn't naturally fall into anyone's area, and "Area Advice" when a team opens up a topic that exceeds their own scope.
3. Consent (not consensus)
Consent is not "everyone agrees." Consent means "no one has a strong enough objection to block this." We do use a poll, but not to count votes — we use a 1-to-5 scale to measure the level of agreement and surface objections, not to let the majority rule.
We use it in two flavors:
High-participation consent: For decisions with deep, company-wide impact. This is our most expensive and slowest method, which is exactly why we reserve it for high-impact decisions that affect many people. The Board sets the boundaries, for example, when we moved offices, they defined the monthly budget. Then a working group produced proposals, collected feedback, evolved them, and the whole company expressed their position for the final decision. Silence is not approval; we explicitly ask people to weigh in, even if it's just "I have no objection."
Lightweight consent: For decisions that are broad but not deep. Participation is optional, anyone who's interested can jump in. We share the proposal, open a window for objections, and if nobody opposes, we move forward. This gives us speed without sacrificing transparency. If nobody engages, that's a signal too, maybe the proposal doesn't add enough value, or we're using the wrong channel.
4. Inform, don't fake-consult
Not everything needs participation. When a decision has already been made through a legitimate process, the right move is to inform, not to fake-consult. One of the fastest ways to kill self-management is to ask for feedback and then ignore it. If you're not going to change course based on input, don't ask for it, just be transparent about the decision and the reasons behind it.
What we explicitly avoid
Decision by Voting. In a company context, majority rule creates losers. And losers become detractors, often generating more resistance than an autocratic decision would have. Instead of voting, we prefer to evolve a proposal through feedback until it's "good enough for now," and then introduce a review point to adjust later. If voting happens at all, it's the cherry on top, not the main course.
The "surprise" approach. Working behind closed doors and then unveiling a finished decision is a recipe for frustration. Adults don't need surprises. Adults need to feel like they're part of the process. The complaints that follow a surprise aren't about the decision itself, they're about not being included.
Why we work this way
We didn't adopt these methods because they're trendy. We adopted them because they solve real problems:
Better decisions. When you include affected people, you get information you wouldn't have had otherwise. Ideas emerge that no single person would have come up with alone.
Less resistance. A person who feels heard is far less likely to resist a decision, even one they wouldn't have made themselves.
Faster execution. It sounds counterintuitive, but participative decisions often execute faster because people already understand and support them. The time you "save" by deciding alone, you spend later managing pushback.
Distributed authority. When people can make decisions within their domain without escalating everything to a founder, the organization scales. The bottleneck disappears.
Resilience. If a shared decision fails, the group adjusts together. If a top-down decision fails, the blame falls on one person and the chances of proactive correction drop.
The real principle behind all of this
Transparency is the foundation. Every method we use, from role-based decisions to high-participation consent, works because information flows openly. People know what's being decided, who's deciding it, and how they can participate.
Horizontal doesn't mean structureless. It means fewer hierarchical levels, clearer roles, and intentional decision-making processes that match the weight of each decision.
Not everyone decides on everything. But everyone knows how things get decided.
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