Free AI prompt tools

Start From The Job, Not The Tool

Ten tools is enough to cause hesitation. The shortcut is to ignore the names and ask two questions: what am I producing, and what stage am I at?

If you're producing text — an article, an email, code, an analysis — you want a tool that shapes instructions. If you're producing an image, you want a tool that shapes description. Those are genuinely different crafts, and mixing them is why people's Midjourney prompts read like memos and their ChatGPT prompts read like tag soup.

Stage matters just as much. A blank page needs a generator. A prompt that half-works needs an optimizer. A prompt that works but costs too much needs a counter. Picking the right stage saves more time than picking the perfect tool.

Text Prompts Versus Image Prompts

Text models respond to instructions. You assign a role, give context, set constraints, and specify a format. Grammar helps. Sentences help. The model is following directions.

Image models respond to description. There is no role and no reasoning — you are listing attributes and weighting them. Subject, medium, lighting, lens, composition, mood, colour. Sentences don't help; precise nouns do.

The image prompt generator handles that vocabulary for you, and the negative prompt builder handles the other half — telling a diffusion model what to keep out of the frame, which often fixes more than adding detail does. The image prompts guide covers why the two halves work together.

Which Tool When

• Writing anything long-form. Draft with the general prompt generator, then run it through the prompt optimizer. If you already have a prompt that's close, skip the generator and use the rewrite tool.
• Coding and structured output. You need the same shape back every time, so use the JSON prompt builder to lock the schema, and the system prompt builder for behaviour that should persist across an entire session or product.
• Design and illustration. Go straight to the image tools, or to a model-specific generator — Midjourney, Stable Diffusion, or DALL·E — since their syntax diverges sharply. For UI and brand work, the design prompt generator is aimed at mockups and concepts rather than photography.
• Research and analysis. Use chain-of-thought prompting when you need to see the steps and catch a wrong assumption early. Guessed answers and reasoned answers look identical until you make the model show its work.
• Cost control. Before you run a prompt ten thousand times, put it through the token counter. A 400-token system prompt you could have written in 150 is real money at scale, and it eats context you'd rather spend on input.
• Quality checks. The prompt analyzer tells you what's missing — no role, no format, ambiguous scope — before you burn requests discovering it.

Model-Specific Generators Versus The General Optimizer

The optimizer improves prompt *structure*: clearer role, tighter constraints, explicit output format, less filler. That improvement transfers everywhere, because every model benefits from an unambiguous instruction.

Model-specific generators go further and encode *conventions*. Claude responds well to XML-ish tags and long, explicit context. ChatGPT tends to want clear step lists and short system framing. Gemini handles long multimodal context differently again. Midjourney has actual parameters — aspect ratio, stylize, version — that are syntax, not suggestions.

Use the general optimizer when you don't know where the prompt will run, or when you want one prompt to behave consistently across several models. Use the model-specific generator when you've committed to a target and want the last 20% of quality that only comes from matching its habits.

Whichever route you take, save the result in the prompt library once it works. And if you want the reasoning behind these conventions rather than just the output, the advanced prompt engineering guide and Anthropic's own prompting documentation both go deeper than any generator can.