This article comes at the perfect time, as I've been grappling with similar issues of context management and the temptation to just 'ship code faster' instead of truly understanding the underlying logic when using AI tools for development. It realy echoes your consistent focus on using AI as a tool for deeper learning and leveling up skills, rather than just a quick fix, which is something I truly appreciate about your approach.
I love this… yes! I have been experimenting with Meta prompts for about three months now, really opens up any model. Most of what I see is; people restraining the answer or framing the answer, prior to it utilizing its full multi domain capabilities. “By asking it to reason and explain its thinking (this is what the user wants), then reflect on its reasoning and correct by clarifying (what you want, or checking its own output, “prior to final output & correct it) then ask it to take action on that request ( fix, create) you force the model to structure and automate its reasoning output. In short ( think about how you think, put that into steps, write out those steps, ask the model to take those steps) = full multi domain answers. Keep up the good work 👍
What I like: your emphasis on learning and mastery, rather just efficiency/effectiveness. So many of us are trying to figure out -- at what level of abstraction should we be trying learn? It may not make sense to dig into learning code; we are trying to take advantage of the advances in AI coding tools. On the other hand, there is SOME level of understanding of the operations and structures of knowledge of these models and tools that makes sense for us to learn. So, thanks!
This article comes at the perfect time, as I've been grappling with similar issues of context management and the temptation to just 'ship code faster' instead of truly understanding the underlying logic when using AI tools for development. It realy echoes your consistent focus on using AI as a tool for deeper learning and leveling up skills, rather than just a quick fix, which is something I truly appreciate about your approach.
I love this… yes! I have been experimenting with Meta prompts for about three months now, really opens up any model. Most of what I see is; people restraining the answer or framing the answer, prior to it utilizing its full multi domain capabilities. “By asking it to reason and explain its thinking (this is what the user wants), then reflect on its reasoning and correct by clarifying (what you want, or checking its own output, “prior to final output & correct it) then ask it to take action on that request ( fix, create) you force the model to structure and automate its reasoning output. In short ( think about how you think, put that into steps, write out those steps, ask the model to take those steps) = full multi domain answers. Keep up the good work 👍
You may find this useful:
https://open.substack.com/pub/karmalane1/p/are-your-prompts-too-confined?r=53vulf&utm_medium=ios
“I decided to stop fighting the AI’s problem-solving drive and to redirect it instead.”
This is AI fluency.
What I like: your emphasis on learning and mastery, rather just efficiency/effectiveness. So many of us are trying to figure out -- at what level of abstraction should we be trying learn? It may not make sense to dig into learning code; we are trying to take advantage of the advances in AI coding tools. On the other hand, there is SOME level of understanding of the operations and structures of knowledge of these models and tools that makes sense for us to learn. So, thanks!