Really enjoyed this. Devs definitely don't get enough credit.
Devs my hat is off to you
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That’s interesting that you have Claude create the exact prompt for you! 😸
I often use Claude in a similar way. First, I ask it to help me create a specification or design plan, then I review and adjust it before moving on to implementation.
Of course, before starting the actual work, I also define agent rules and skills, such as structure, design patterns, coding style, and other project-specific decisions.
It usually helps me get more consistent results, but I still review the output every time because AI can sometimes take a tactical approach just to complete the task, rather than a strategic approach that fits the whole project.
@[nyaomaru] I also setup a system wide prompt that tells Claude not to just agree or appease me and I also told it that I am not the source of fact and that it is to evaluate its suggestion and my request and notify me if my thinking or process is wrong and to explain why. It got so much better when it stopped telling me I was right and started to be an advisor than and yes item
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I actually find using multiple different "agents" works better, especially for the planning and spec work. Claude will do Claude things. ChatGPT does it's thing. Gemini does, well, whatever. However, bounce an idea off of one, take that initial spec doc and pass it to the others until everyone, including yourself, says "yeah, that's in good shape." At that point I'll pass it to Claude Code or Cursor to start implementing things.
And it generally goes beyond a single "spec" doc. Things like a folder of Architectural Decision Record (ADR) documents helps tremendously. Include in the planning doc not only what to build, but what is out of scope. Come opinionated with the tech stack you want to use, frameworks, database, everything. Write that into the spec too.
Much like conversations in the UI version of ChatGPT, Gemini, Claude, etc. not being specific about things allows the LLM to infer intent. Similar to saying "I like blue" and getting a Navy Blue UI element when you meant or were hoping for Cobalt Blue. Being specific also tends to reduce token expenditure because the model doesn't have to make those decisions.
Just my experience and two cents here. Happy to discuss further as well as I do this quite a bit. :-)
@[Ken W. Alger] these are all good points and a lot that I do. When I am close to a decision I'll get a full breakdown of what it thinks is needed and then I'll take that to ChatGPT and see. Theres been times it's went back and forth but eventually come to an agreement.
This is great info and advice. Thanks for sharing.
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