OpenCV 5 introduces significant architectural changes to improve vision performance and better utilize modern hardware. In addition to support for new features like vision-language models, there are paradigm shifts such as a revamped Graph API (G-API), universal intrinsics and the removal of C language support. Engineering teams need to take advantage of these changes without missing opportunities for improvement or going down the wrong path. Enter generative AI—it can be either your best friend or your worst enemy when rewriting code. In this talk, we explore a pragmatic workflow using Claude Code to guide the migration process. We move beyond basic code generation to focus on using generative AI as a learning and architectural tool. We’ll explain how to provide the right context and environment to an LLM so it understands OpenCV 5’s specific changes, rather than falling back on legacy 4.x knowledge. We’ll also share practical prompting techniques to encourage modern “G-API thinking” instead of repeating old patterns. In addition, we’ll show how to handle common pitfalls, such as when an LLM suggests ignoring compiler warnings, and how to use LLMs to quickly demystify complex new OpenCV features like multimodal inputs. Attendees will leave with a realistic perspective on where LLM coding assistants excel in the migration process and where human oversight is critical.