"The previous article in this series on developing with Artificial Intelligence (AI) for learning professionals covered the first steps of the initial setup: fundamental elements and editing your code. The remaining components of the architecture are recommended for working with multiple files and more complex projects. While it may feel like setting up an environment slows you down, believe me, it's a one-time investment worth the effort when you start debugging."
"Example When working with Articulate Storyline, I published the initial course locally, and then used that published folder as the source for my AI-assisted development. I explained to the tool (Windsurf) where the files are, what is republished by Storyline every time (so it shouldn't touch it), and what files it can edit. This way, AI could see the whole project, not only the JS snippet it was writing."
"Once you've set up your editor of choice and you're connected to an LLM, think about what kind of projects you're going to work on and who your audience will be. STORE Even if you're working alone, storing the last functioning version of your source code (along with previous, committed versions) can save you time and frustration. If you plan to collaborate with others on the code or share it with others, then an application dedicated to storing and source control is a must."
Set up a development environment and connect to an LLM before starting AI-assisted learning projects. Define project scope and target audience to guide tooling and workflows. Use source control to store the last working version and committed history to enable rollback, collaboration, and sharing; GitHub offers free private and public repositories with versioning and collaboration features. When integrating AI with complex projects, point the AI at the published project folder, mark files that are republished and must not be edited, and expose the full project context so AI can access screenshots and logs. Investing time in setup reduces debugging time.
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