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I've started noticing a growing trend among AI users. Instead of using local AI to replace ChatGPT, they are using it before ChatGPT. A quick browse through Reddit communities like r/LocalLLaMA and r/WritingWithAI shows power users actively programming tools like LM Studio to act as 'prompt-engineering assistants'—forcing the AI to ask them 5 questions about their goals before generating the final prompt.At first, that sounded backwards. Why would anyone run a prompt through a smaller AI model before sending it to one of the most powerful AI systems available? The process of prompting the AI to ask questions works, but why would you start with local AI?After testing the workflow myself, I realized using this technique is one of the best ways to avoid over usage limits while ensuring the cloud AI delivers the best answer possible.Don't let local AI scare you off
(Image credit: LM Studio screenshot)Before we go any further, don't let the phrase "local AI" send you running for the hills. A few years ago, running AI models on your own device often required deep technical know-how and expensive hardware. Today, that is no longer the case.Free tools like Ollama, and GPT4All make it surprisingly easy to run small AI models directly on a standard laptop. Some browsers, like Brave, even let you plug local models right into their built-in sidebars.To be clear, you're not replacing ChatGPT completely in this experiment. Instead, the goal is to use a smaller AI as an editor before ChatGPT ever sees your prompt. One tool helps shape the assignment for free; the other completes it.The problem with most ChatGPT promptsOne of the biggest weaknesses of any large language model is that it assumes it understands what you mean. Although ChatGPT-5.5 has gotten a lot better at not assuming and Claude Opus 4.8 promises to deliver more accurate responses, these models will push back. Even still, if you ask a generic or broad question, you're going to get a mediocre answer in response. That's because LLMs are designed to generate useful responses based on the information they receive. The real problem is that users are notoriously bad at providing complete context.Get instant access to breaking news, the hottest reviews, great deals and helpful tips.The local AI prompt I usedTo test this multi-model workflow, I loaded a local model and gave it one job: improve my prompt before ChatGPT ever saw it.Here is the exact prompt I used: Act as a prompt engineer. I am going to give you a task for ChatGPT. Before answering, analyze it, tell me what context or information is missing, identify any assumptions I'm making, and ask me 5–6 clarifying questions that would significantly improve the final result. Do not attempt to complete the task yet. Focus only on gathering the information needed to create the best possible prompt.Instead of immediately generating answers, the local model became an interviewer. And that is where things got interesting.When I asked for help planning a vacation, the local model didn't suggest destinations. Instead, it asked: What is your budget? How many people are traveling? Are you driving or flying? What are the ages of the children? Do you prefer relaxation or activities? How many days will you be away?Only after answering those questions did I send the complete, updated context to ChatGPT. The final response felt dramatically more tailored. And while I realize this is a simple example, the point is, by having the local AI ask you questions, you won't waste your time or tokens with a bigger model doing the same thing. By having a local model ask you the right questions, you are curating a better and final prompt for ChatGPT.Which local AI models can do this?
