I would characterize good prompting as: write out your whole problem you're trying to solve, then think to yourself what the clarifying questions would be if y

I would characterize good prompting as: write out your whole problem you're trying to solve, then think to yourself what the clarifying questions would be if you were a junior trying to solve it. Better yet - ask the LLM to ask you challenging clarifying questions for several rounds. Then, take all that information and re-compile it back into a list of all the important components of the project, and re-read it to make sure there's no particular ambiguous part or weird part that would be over-emphasized by the language you used. Then, emphasize the core concerns again, and tell it how you'd like it to output the response (keeping in mind that it will always do best with a conversation-style format with loose restrictions). Never let a conversation stray too long from the original goals lest it start forgetting. Once that's all done, you basically have a well-structured question you could pass to an underling and have them completely independently work on the project without bugging you. That's the goal. Now, pass that to o1 or Claude, depending on whether it's a general-purpose task (o1) or a code-specific task (Claude), and wait for response. From there, have a conversation or test-and-followup of whatever it spits out, this time with you asking questions. If good enough, done. If not, wrap up whatever useful insights from that line of questioning and put it back into the initial prompt and either re-post it at the end of the conversation or start a fresh conversation. I find 90% of the time this gets exactly what I'm after eventually. The few other cases are usually because we hit some cycle where the AI doesn't fully know what to change/respond, and it keeps repeating itself when I ask. The trick then is to ask things a different way or emphasize something new. This is usually just a code-specific issue, for general problems it's much better. One other trick is to ask it to take a step back and just tackle the problem in a theoretical/philosophical way first before trying to do any coding or practical solving, and then do that in a second phase (asking o1 to architect code structure and then Claude to implement it is a great combo too). Also if there is any way to break up the problem into smaller pieces which can be tackled one conversation at a time - much better. Just remember to include all relevant context it needs to interface with the overall problem too. That sounds like a lot, but it's essentially just project management and delegation to somewhat-flawed underlings. The upside is instead of waiting a workweek for them to get back to you, you just have to wait 20 seconds. But it does mean a ton of reading and writing. There are certainly already some meta-prompts where you can get the AI to essentially do this whole process for you and assess itself, but like all automation that means extra ways for things to break too. Let the AI devs cook though and those will be a lot more commonplace soon enough... [Edit: o1 mostly agrees lol. Some good additional suggestions for systematizing this: https://chatgpt.com/share/6775b85c-97c4-8003-bd31-ee288396ab5d https://chatgpt.com/share/6775b85c-97c4-8003-bd31-ee288396ab... ]