PrompTessor

I built PrompTessor after after repeatedly seeing great AI tools produce disappointing results because the prompts were unclear or incomplete. What started as a prompt optimizer has grown into a complete workspace for generating, analyzing, refining, reverse-engineering, saving, and reusing prompts across text, image, and video AI models. PrompTessor is designed to help anyone turn a rough idea into a structured, ready-to-use prompt without needing to become a prompt engineering expert. I would love to hear how you use AI prompts today, what slows you down, and what you would like us to improve next. Your feedback will directly help shape PrompTessor.

The structured prompt workspace and quality metrics look especially useful for turning rough ideas into repeatable workflows. The library plus reverse-prompt features should save a lot of iteration time.

The combination of prompt evaluation with clear metrics and token estimates is especially useful for iterating on production prompts. How do you handle versioning when a prompt is shared across different models?

Its good Study prompt structure, analyze examples, understand effective prompting patterns, and create clearer instructions for learning and research.

Thats a good thing to store data prompts in one place it gets scattered everytime i pick the prompts.

The combination of structured prompts, quality metrics, and estimated token usage is a practical workflow improvement over keeping snippets in scattered notes. Clear examples of the evaluation metrics would help teams adopt it consistently.

The combination of prompt evaluation, token estimates, and a reusable library sounds useful for teams that want repeatable output instead of one-off prompt tinkering. The cross-model workflow is especially interesting—can users compare the same prompt across models while keeping the evaluation criteria consistent?

"Get better results without paying for a more expensive model" is a sharper pitch than most prompt tools manage — it names a cost, not a feature. The Reverse Prompt idea is the interesting part to me: deriving a prompt pattern from an existing image or video output is genuinely useful for anyone doing image-to-video work, where the gap between what you asked for and what you got is the whole job. Question: when you estimate token usage, is that per-model, or a single estimate? The same prompt costs very differently on Claude vs. GPT vs. Gemini and that's usually the number people actually want.

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Super insightful breakdown! Having a dedicated system for prompt history, scoring metrics, and reverse engineering solves one of the biggest bottlenecks in AI workflows today. Thanks for sharing such a practical overview!"

A thoughtful way to keep prompt experiments organized. The combination of structured generation, evaluation, and a reusable library should help teams iterate more consistently.

This is a really cool idea, just a question though: did you get all of the knowledgebase of ai prompting from one source? multiple sources? or just years of experience? or all of the above?

This looks super useful for anyone working with AI daily

if you spend a lot of time writing and tweaking prompts, the reverse engineering and token tools alone justify it. If you're a casual user who writes a prompt once a week, you probably don't need this. It's a power user tool, and it's good at what it does — just know what you're signing up for.

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PrompTessor turns ideas into scored prompts with clarity metrics and estimated token usage, then keeps them in a Prompt Library for ChatGPT, Claude, and Gemini. If you want to spend less on AI at work, take a look at pzero.studio.

Reverse Prompt is the bit I'd actually use. Copying the "vibe" of an image you liked is way harder than writing a prompt from scratch. One thing I'm curious about: when you score a prompt 8/10, is that from reading the prompt text, or do you actually run it and check the output? Big difference in how much I'd trust the number.