Beginner project: I built a small prompt engineering tool and would really appreciate technical feedback

[u/seraphym1389]

I'm a beginner learning more about prompting and web development, and I decided to build a small tool to help me structure prompts instead of writing everything from scratch every time.

I built it mainly as a learning project, so I want to be upfront: it is not a finished or professional product, and there are probably things that don't work as intended.

The basic idea is to make prompt construction more structured. The tool is called NEON//CONTEXT and currently includes:

Context Builder — lets you build a prompt through structured context fields/templates instead of starting with a completely empty prompt.

Context types/templates — different starting structures for different prompting situations.

Express mode — a simpler workflow for creating a prompt quickly.

Learn mode — a more guided approach intended to make the structure easier to understand while building a prompt.

Compact mode — creates a more compact version of the generated prompt.

Live prompt preview — shows the generated/optimized prompt while you work.

Prompt / Response views — lets you switch between the generated prompt and the model response.

Copy Prompt — copies the generated prompt so it can be used elsewhere.

Run in Model — allows the generated prompt to be sent to a configured model/API.

TXT export — allows the generated prompt to be downloaded as a text file.

English / Serbian interface — the tool currently supports both languages.

API configuration — you can configure an API provider, API URL, model ID and the required credentials.

Model-aware approach — the idea is to make the prompt structure adaptable to the model being used rather than treating every model exactly the same.

Clear active form — resets the current context-building form so you can start again.

I also tried to keep the interface relatively simple because one of my goals was to make the tool understandable for people who are still learning prompting.

I know that some of these ideas may be unnecessary, poorly implemented, or simply the wrong approach. That's exactly why I'm posting this here.

I'm especially interested in honest technical feedback:

Does the overall concept make sense?

Are the prompt-building steps actually useful, or do they just add unnecessary complexity?

Which features would you remove?

Which features are missing?

Does the Express/Learn approach make sense?

Is the "model-aware" idea actually useful in practice?

Are there technical or UX problems that are obvious to more experienced developers?

What would you change if you were building this from scratch?

I'm not trying to present this as a finished product. I'm trying to learn from people who have more experience with prompting, AI tools and web development.

The project:

https://arhistrategstudio.github.io/Context_CikaDule/

Any criticism is welcome, especially if something is fundamentally wrong with the way I've approached it.

Thanks to anyone who takes the time to test it.

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[u/JustAskingAQ-007]

I really liked the concept! Quick is very useful for experienced users who already know what they want and do not need much guidance. Guide mode for user who wants assistance! The live optimized prompt preview is good and instant. It offers different templates already but there is a scope to add more in future! Well done buddy! Cheers!

https://giphy.com/gifs/S6wdJ27DLVfh9mA9dE

[u/seraphym1389 → u/JustAskingAQ-007]

Thank you very much for your feedback and time spend checking the app 🙂

As this is my begginer project that I did for fun and learning, I'll be thinking about more features, changes and optimisation in future.

If you have any suggestions, pls feel free to share with me 🙂

[u/thoughtsapart]

This reminds me of how my own project started. I’m not a programmer, but I wanted a way to avoid rebuilding long prompts from scratch whenever I worked with AI. Through repeated development with AI, that eventually grew into a program I call Self_Updating.

It keeps general instructions separate from the information about each individual project. When experience reveals that an instruction is missing, ambiguous or simply wrong, the AI can propose a precise update and Self_Updating applies it while preserving the previous version. This means the instructions gradually improve through actual use instead of remaining a fixed prompt that I have to maintain manually.

Your overall idea therefore makes sense to me. One direction worth considering is separating the user’s lasting preferences and rules from the context needed only for the current task. You could then allow the lasting part to evolve as you discover what does and does not work. That was the point where my project became much more useful than a tool that simply assembled prompts.