When did AI solve my issue?

A developer recounts how AI successfully resolved a programming issue with trigger.dev, contrasting with previous failures. The key was providing detailed context and web resources to ChatGPT, which then found the relevant configuration page. The developer emphasizes that in-depth knowledge and context are crucial for effective AI-assisted debugging.

In the last two blogs, I shared how AI failed to solve a few issues in programming and the value of self-search; today, I am going to share the opposite. The main goal is to show how you can learn from AI and use it as effectively as possible. This all started when I was using trigger.dev and got the following error: Node.js 21 detected without native WebSocket support. Suggested solution: For Node.js < 22, install "ws" package and provide it via the transport option: import ws from "ws" new RealtimeClient url, { transport: ws } using trigger.dev The error clearly asks me to either install the ws package or update Node.js. But since I did not have a full experience with trigger.dev I could not figure out how to do that. My approach to debugging is based on methods: With this error, I went with AI first, asking ChatGPT about it; then I tried Googling it which used to work before the AI age , but I could not find any data. With ChatGPT, I gave it two extra points to help it get the right answer; I shared that I am using trigger.dev, added the web resources, and asked for a solution based on my tech stack. By giving ChatGPT context and a web search, it was able to find the config page on trigger.dev https://trigger.dev/docs/config/config-file node-js-versions and get the results I wanted. With this experience and the ones I had before, the most important thing when using AI was the context and knowledge I had to provide. The in-depth knowledge can help the user and AI to find the optimal solution, yet going blindly might lead you to a black hole without knowing how to return.