AI is becoming an essential tool in modern software development, and my experience with it has significantly enhanced my workflow. From automating repetitive tasks to improving the quality of my code, AI has introduced several tangible benefits to my day-to-day work. Over time, my usage has evolved from experimentation to more structured, intentional integration into feature development. Below are some of the ways I currently leverage AI in my development process.
1. AI-Assisted Code Writing
My first introduction to AI was through ChatGPT, which I found valuable for general-purpose questions, concept clarification, and problem-solving. It allows me to explore new frameworks, troubleshoot issues, and reason through implementation ideas with relatively low friction. I’ve also found AI particularly helpful when working with older technologies where documentation is sparse or difficult to navigate.
However, one early limitation was that AI tools were not always up to date with the latest advancements in specific frameworks. This gap has narrowed significantly with the introduction of tools that provide real-time or framework-specific context.
2. Using AI for New Feature Development
More recently, I’ve started using AI tools such as the Claude Code CLI when creating new features. This has been a noticeable shift in how I approach development. With the addition of tools like Context7 MCP and framework-specific MCPs (such as Laravel Boost), AI is now far more aware of current framework conventions, APIs, and best practices. This improved contextual accuracy makes AI significantly more reliable when working in modern stacks.
I’ve found that Claude Code CLI works particularly well when paired with agents and when tasks are broken down into smaller, well-defined changes. Larger, vague tasks tend to increase the risk of hallucination or incorrect assumptions. By decomposing features into minor, incremental steps, the AI produces more accurate and usable results, while remaining easier to validate.
3. Code Quality and Documentation
AI continues to be extremely helpful in improving code quality and documentation. JetBrains’ built-in AI tools help streamline my development process by suggesting optimizations and generating documentation inline. This reduces errors and helps keep the codebase readable and maintainable over time. Similarly, tools like GitHub Copilot assist with generating repetitive or boilerplate code based on project context, improving consistency and saving time.
4. AI Contextual Awareness in the IDE and CLI Tools
One of the most impactful uses of AI in my workflow is the ability to provide rich, evolving context—both through deep IDE integrations and through modern AI CLI tools. IDE-based solutions, such as JetBrains’ AI features, can understand the broader context of the application and provide relevant suggestions when prompted about specific parts of the codebase.
Beyond the IDE, tools like Claude Code CLI, OpenAI CLI, and Gemini CLI also support contextual awareness by allowing developers to explicitly supply context, often in the form of .md files or structured documentation. This makes it possible to describe architectural decisions, coding standards, domain knowledge, or feature-specific requirements directly to the AI. Over time, this context can be built up and refined, resulting in more accurate, consistent, and project-aware responses.
This combination of IDE-driven context and developer-supplied contextual files elevates AI from a simple code generator to something much closer to a collaborative development partner—one that understands not just isolated prompts, but the evolving shape and intent of the system.
That said, this collaboration still requires discipline. AI-generated code must always be reviewed carefully.
My Final Take
The benefits of AI in software development have exceeded my initial expectations. From reducing repetitive tasks and improving code quality to generating assets and accelerating feature development, AI has become an invaluable part of my workflow. However, it is critical to approach AI with caution. It cannot be blindly trusted.
When using AI, I make it a strict rule to read and understand every single line of code it produces before committing any changes. AI can and does hallucinate, especially when tasks are too broad or poorly scoped. I treat AI as a pairing partner—one that can accelerate and enhance my work—but I retain full responsibility for the final outcome.
Used thoughtfully, AI reduces context switching, saves time, and minimizes the need to constantly search the web for solutions. As the tooling continues to mature, I see AI not as a replacement for developers, but as a powerful extension of the modern development toolkit.