Engineering teams increasingly rely on AI tools such as Claude Code, Cursor, Codex, and GitHub Copilot to generate code, debug applications, and automate repetitive tasks. This shift has introduced a new challenge: understanding and documenting how AI-assisted changes are made. Traditional workflows—writing code manually, committing changes to Git, reviewing pull requests, and merging into production—are no longer sufficient. Developers now spend more time reviewing AI-generated code, refining prompts, validating suggestions, debugging automated changes, and collaborating on implementations, much of which occurs outside version control. Prompt history, terminal commands, browser interactions, and debugging steps often disappear once a task is complete. An effective AI Development Workflow captures the entire journey from prompt to production, including AI prompt creation, code generation, terminal execution, browser testing, debugging sessions, code reviews, documentation, and team collaboration. Documenting every step makes onboarding easier, improves knowledge sharing, and speeds up debugging. Visual documentation is important because screenshots rarely capture complete context; for example, a developer using Claude Code to fix a production issue may generate multiple iterations, execute several terminal commands, modify configuration files, and test different approaches. Without a recorded workflow, teammates only see finished code, not the reasoning behind it. Visual documentation shows AI prompts, code changes, terminal output, browser interactions, debugging processes, and final implementation. This context is valuable during code reviews and maintenance. For remote teams, sharing complete AI-assisted workflows allows asynchronous review, reducing repetitive explanations, speeding