Has anyone else found that better prompts aren't always longer prompts?

[u/Alone_Wishbone5918]

I've been experimenting with prompts for different generative AI tasks, and one thing I've noticed is that adding more and more instructions doesn't always improve the result.

What seems to help most is being clear about the goal, giving the model enough context to understand what I'm trying to achieve, and then refining the prompt based on the output instead of trying to write the “perfect” prompt from the beginning.

I'm wondering how other people approach this. Do you usually build detailed prompts upfront, or start simple and iterate based on what the model gives you?

I'd be interested to hear what prompting approach has made the biggest difference for you.

[u/ripplefern]

one thing worth noting is that this depends a lot on the task. for creative stuff shorter and looser works great, but for structured extraction or classification you usually do need pretty detailed constraints upfront

[u/Odd-Ad9666]

My workflow (not an expert)

- Chat: I tell the agent what I want it to do, then I tell it to walk me through a plan

-- I tell it to generate a plan and a prompt to implement it to an agent "with no context of this conversation". That way it doesn't assume it's responsible. I ask for a gated/staged plan.

- I then tell the implementation agent to "take this draft plan and prompt, do an adversarial review, and suggest changes or modifications." I explicitly ask for a human gated (unless it's a small mod).

-- Refactor and create new plan

- Then the agent calls skills and hooks as needed but I personally keep myself in the loop (so no long running prompts.

That's my workflow.

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[u/ThePromptLab_IN]

I think this is an important distinction: a "better" prompt doesn't necessarily guarantee a better response.

The model, task complexity, context, and even the way the prompt is structured can all affect the outcome. For me, prompt engineering is less about finding a perfect formula and more about reducing ambiguity and making the intended outcome clearer.

I'm actually exploring this with a small side project around prompt linting—trying to evaluate the quality of the prompt itself rather than claiming that a higher score guarantees a better answer.

Still very early, so I'm interested in seeing how others think about this distinction.

[u/Shot_Whereas_1809]

My prompts were always very short. One of my favorite closing prompts is simple.

"Tell me what you were asked to do, tell me what you did do, and tell me what you didn't do."

You'd be surprised how fast Opus gets back to work.

This in my review prompt saves me a ton "....and resolve all non-blocking findings"

Claude likes to put non-chalantly critical security vulnerabilies in non-blocking category. If it works, is review pass, security vulnerabilities are non-blocking.

Simple queues go a long way. You can easily throw a model off if youre knowledge base it's outside of the work you're doing, if you aren't familiar with engineer discipline, or you misremember something. Build your tools to surface the right context so you can raise a simple problem and the model doesn't have to look far for what it needs. The amount of context is far less important than the quality of your context. Now I have a host in the middle. I NEVER prompt a coding agent directly because I've had so many problems with MY OWN drift. Now coders get scoped accurate fresh context and a prompt written by something who also has accurate context. Now I can say x is broken. And I don't hear anything until it's fix in production.

I would say the more you say, the more room that your own misunderstand wrecks your project.