I’ve started using the chat feature in Github Copilot in IntelliJ. I wanted it to add some logging to my code for me, since it was a tedious task. I started off with a few relevant files and an explanation of what I wanted. Naturally it didn’t get it right on the first try, I don’t think any humans would either. But I could continue as conversation explaining what I thought was wrong and how I wanted it to actually be. I even realised that I didn’t know exactly what I wanted before I had seen some of the suggestions.
Once I was happy with the result I added another file to the chat and asked it to do the same with this file. I had a handful of files that were structured very similarly and all needed the same kind of logging. It did a great job and I could use the response without further editing. I tried to add more files but realised that the replies got slower and slower, so instead I reverted the conversation back to the state where I had initially been happy with the results and asked it to do the same thing but this time to a different file.
I find that it takes some practice to get good at getting the best results from LLMs. One great place to start is the prompt engineering guide by OpenAI https://platform.openai.com/docs/guides/prompt-engineering
When using something like GPT-4 for developing I try to think of it as a junior developer or a grad student. With a search engine you need to include the correct keywords to get the best results. For LLMs you need to set the right mood by writing a good prompt and holding a conversation before getting to the point. I also find that GPT-4 is fairly good at answering factual questions, but it’s much more useful and powerful when used to create things or discuss an approach.
