How We Use AI in Our Own Development Process
We practice what we preach. Here is an honest look at how AVARC Solutions uses AI tools in our daily development workflow, what works, what does not, and what we have learned.
Introduction
When we advise clients on AI adoption, we speak from experience. Our own development team uses AI tools daily, not as a gimmick but as a genuine productivity multiplier. Some tools have transformed how we work. Others turned out to be more hype than help.
This article is an honest account of how we integrate AI into our development process at AVARC Solutions, including the mistakes we made along the way and the practices that stuck.
Code Generation and Pair Programming with AI
AI-powered code editors are the most visible change in our workflow. We use them for boilerplate generation, writing tests, exploring unfamiliar APIs, and as a thinking partner when working through complex logic. For repetitive patterns like CRUD endpoints, form validation, and data mapping, AI generates eighty percent of the code that we then review and refine.
The critical insight is that AI pair programming requires the same skills as reviewing a junior developer's code. You need to understand what the code should do, verify it does that correctly, and catch subtle bugs that look plausible at first glance. Developers who blindly accept AI suggestions produce worse code, not better code.
Automated Code Review and Quality Checks
We run AI-powered analysis on every pull request before human review. The AI checks for security vulnerabilities, identifies potential performance issues, flags inconsistencies with our coding standards, and suggests improvements. This does not replace human code review but makes it faster and more focused.
The biggest win has been in catching issues that humans consistently miss: unused imports, inconsistent error handling patterns, and subtle type mismatches that TypeScript's compiler does not flag. These are the boring, repetitive checks that humans skim over after reviewing hundreds of lines.
Documentation and Knowledge Management
Writing documentation is the task developers avoid most. AI has made it dramatically easier. We generate initial documentation from code, including API references, component prop descriptions, and architecture decision records. A developer then reviews and enriches the generated content with context that only a human would know.
We also use AI to maintain an internal knowledge base that answers questions about our codebase. Instead of interrupting a colleague to ask where a specific feature is implemented, developers query the AI assistant which searches our code, documentation, and past pull requests to provide contextual answers.
What Did Not Work
Fully autonomous code generation without human oversight failed for us. When we experimented with letting AI generate entire features end to end, the code looked correct but contained subtle architectural decisions that did not align with our system's design principles. The cleanup cost more time than manual development would have.
AI-generated tests also required careful handling. Generated tests often test the implementation rather than the behavior, making them brittle and useless for catching actual regressions. We now use AI to scaffold test structure but write the assertions ourselves to ensure tests validate business logic, not code structure.
Conclusion
AI in development is not about replacing developers. It is about removing friction from the parts of development that slow us down: boilerplate, repetitive reviews, documentation, and knowledge lookup. The developers who get the most from AI are the ones who understand the code deeply enough to use AI as a tool rather than a crutch.
Curious how AI could accelerate your development team? AVARC Solutions helps teams adopt AI development practices with training, tooling recommendations, and workflow design.
AVARC Solutions
AI & Software Team
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