AI tools are everywhere in software development. They can write code, generate tests, suggest improvements, and answer questions. But using AI effectively isn’t about replacing developers. It’s about enhancing how we work while keeping humans in charge of important decisions.
Here’s how we use AI at Zofwe, when we use it, and when we don’t.
Our approach: AI as enhancement, not replacement
We use AI to speed up parts of development that used to be slow, not to replace the thinking that makes software good. AI helps us explore more options, write better first drafts, and catch things we might miss. But humans make every important decision.
This means:
- AI helps us explore options, not replace judgment
- We keep sensitive data safe and private
- We design systems where people can understand and override automated behavior
- We focus on practical applications that actually save time
The result is products that feel smarter and more helpful while still being predictable and trustworthy.
Where AI helps
Exploring options faster
When we need to solve a problem, AI helps us explore different approaches quickly. Instead of spending hours researching solutions, we can ask AI to show us several options, then evaluate them ourselves.
For example, when choosing how to handle user authentication, AI might suggest three different approaches. We evaluate each one based on our specific needs, security requirements, and constraints. AI didn’t make the decision. It helped us explore faster.
Writing better first drafts
AI is great at writing initial versions of code, documentation, or tests. We use it to create first drafts, then refine them ourselves. This is especially useful for:
- Boilerplate code that follows patterns
- Test cases that cover common scenarios
- Documentation that explains how something works
- Comments that clarify complex logic
The key is treating AI output as a starting point, not a final product. We always review, refine, and improve.
Generating test data
Creating realistic test data is tedious but important. AI helps us generate test data that covers edge cases and realistic scenarios. This saves time and helps us catch bugs we might have missed.
Finding edge cases
AI can help identify edge cases we might not have considered. When we describe a feature, AI can suggest “what if” scenarios that help us think through potential problems.
Again, AI doesn’t replace our thinking. It enhances it by helping us consider more possibilities.
Where we don’t use AI
Making architectural decisions
Architecture decisions affect your product’s future. They require understanding your business, constraints, and long-term goals. AI can’t make these decisions because it doesn’t understand your specific context.
We use AI to explore options, but humans make the final call based on what makes sense for your product.
Handling sensitive data
We never put sensitive user data, API keys, or proprietary information into AI tools. These tools are powerful, but they’re not private. Anything you put in might be used for training or stored in ways you don’t control.
We use AI for code patterns, documentation, and general problem-solving, but not for anything that involves real user data.
Replacing code reviews
Code reviews are about more than finding bugs. They’re about sharing knowledge, ensuring consistency, and catching problems before they reach production. AI can help catch some issues, but it can’t replace the human judgment and team knowledge that code reviews provide.
We use AI to help with code reviews, not replace them.
Making product decisions
Product decisions require understanding users, business goals, and what actually matters. AI can’t make these decisions because it doesn’t understand your users or your business.
We use AI to help us think through options, but product decisions come from understanding your goals and your users.
Practical examples
Example 1: Building a feature
When building a new feature, we might:
- Use AI to explore different implementation approaches
- Use AI to generate initial test cases
- Write the code ourselves, using AI suggestions as reference
- Review everything ourselves before it goes to production
AI helped us move faster, but we made all the important decisions.
Example 2: Writing documentation
When documenting a feature, we might:
- Use AI to create an initial draft
- Review and refine it ourselves
- Add specific examples from our codebase
- Ensure it matches our style and standards
AI gave us a head start, but we made sure the documentation was accurate and useful.
Example 3: Debugging
When debugging, we might:
- Use AI to suggest potential causes
- Test those suggestions ourselves
- Use our understanding of the system to find the real issue
- Fix it ourselves
AI helped us think through possibilities, but we found and fixed the actual problem.
The principles
Our approach to AI-assisted development follows a few principles:
Humans make important decisions: AI helps us explore and draft, but humans decide what to build and how.
Privacy first: We never put sensitive data into AI tools. User data stays private.
Understand and override: We design systems where people can understand what AI did and change it if needed.
Practical over impressive: We use AI where it actually helps, not just because it’s cool.
Quality over speed: AI helps us move faster, but we don’t sacrifice quality for speed.
The result
When we use AI thoughtfully, we get:
- Faster development without sacrificing quality
- Better exploration of options
- More thorough testing
- Products that feel smarter while remaining predictable
But we also get:
- Human judgment on important decisions
- Privacy and security for sensitive data
- Systems that people can understand and control
- Products that actually help users
How this helps you
When you work with Zofwe, you get a team that uses AI effectively without letting it replace judgment. We move faster because AI helps with the routine work, but we make sure important decisions are made by people who understand your goals and your users.
If you’re building a product and want a team that uses AI thoughtfully, we’d love to talk. We’ll help you build something that’s smart, helpful, and trustworthy.