AI Prompt Improver

An AI prompt improver rewrites the instruction you give an AI so that it produces a better answer. You paste your current prompt, choose the kind of AI it is for, and a language model returns an improved prompt that is specific about role, context, audience, format, length and constraints, then explains what changed and why, offers two alternative versions and gives tips for writing that kind of prompt.

Most weak prompts are weak in the same way: they leave the model to guess. "Write a blog post about marketing", the sample on the page, does not say who the reader is, what kind of marketing, how long, or what the post should achieve. An improved version might ask for 800 words on email marketing for a small bakery owner with no budget, in a friendly tone, with three examples and a checklist at the end. Every added detail removes a guess.

The target setting changes what "better" means. For ChatGPT or another chat model, it adds role, audience and output format. For an image AI such as DALL-E, Midjourney or Stable Diffusion, it adds subject, style, lighting, composition and aspect ratio instead. For a coding assistant, it adds the language, inputs, outputs and edge cases; for a research assistant, scope and how sources should be handled.

The improver itself runs on the Toolz server: your prompt is sent over HTTPS, the server runs Gemma 3 1B, a small open model from Google, with Ollama, and the rewrite streams back within a few seconds. Prompts often contain client names or unreleased plans, so it matters that the text is used to write the reply and then discarded. It is not stored or logged, not used to train any model, and not sent to any third-party AI company. The limit is that a 1B model does not know your full situation, so it can add assumptions that are wrong for you. Replace any invented detail with the real one before you use the prompt.

It is free with no signup, within about 20 runs per 10 minutes. The alternative is learning the pattern yourself: state the role, the task, the reader, the format and the constraints, then give one example of what good output looks like.