Over the past few weeks, I’ve been building a side project called PromptPilot while experimenting with ChatGPT, Gemini, Grok, and other LLMs. One pattern kept showing up: Many users blame the model when the real problem is that the prompt lacks critical information. For example: “Write a blog post about AI” sounds reasonable, but it leaves many unanswered questions: Who is the audience? What tone should be used? What is the objective? What level of technical depth is expected? What constraints exist? To explore this, I built a system that analyzes prompts, identifies missing information, asks follow-up questions, and then generates a structured prompt blueprint. After testing dozens of prompts, I noticed something interesting: The quality improvement often came less from “rewriting” and more from forcing clarification. In many cases, asking 3–5 targeted questions produced a larger improvement than simply feeding the original prompt into a stronger model. This made me wonder whether prompt engineering is partly a communication problem rather than a model problem. I’m curious what others think: Do you find that most poor outputs come from weak prompts or weak models? Have you found structured questioning to be more effective than prompt rewriting? What information do you think users most commonly forget to include? For anyone interested, Link: https://prompt-pilot-rho.vercel.app/ I’m mainly looking for discussion and feedback on the idea rather than promotion. submitted by /u/GriMEaTer875
Originally posted by u/GriMEaTer875 on r/ArtificialInteligence