AI generates README files, API docs, comments, diagrams, and workflow visualizations to make systems easier to understand and share with teams.
Humans must verify accuracy, fill in missing context, and decide what documentation and architectural representations are actually useful to the team.
AI drafts setup steps, feature summaries, and usage examples from the repository structure and code comments to speed up onboarding documentation.
ChatGPT / ClaudeAI helps turn code annotations or specs into structured developer documentation with endpoint descriptions and example requests.
Mintlify / ClaudeAI summarizes services, data flows, and dependencies into an initial system diagram that the team can refine for accuracy.
Swimm / ClaudeI help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
Design and build LLM-powered products and agentic systems
Go from idea to production with a clear implementation roadmap
Build AI with human-in-the-loop in regulated environments
Over the past few months, I’ve been creating projects/applications with AI-powered coding assistants, and the experience has been nothing short of transformative. What started as curiosity has evolved into a fundamental shift in how I approach software development. Here’s what I learned about the capabilities, limitations, and best practices of coding with AI.