Abstraction AI: From Long, Messy Context to an Elegant Build

Project: https://abstractai.ai-builders.space/

When I start a new project, a workflow I often fall into is: I talk with ChatGPT for many rounds until I end up with a very long context. In the past, I would throw that entire conversation directly into a coding AI (like Augment Code or Claude Code) and ask it to implement the project “based on all the context”. It feels like there’s enough information—after so many back-and-forth rounds, the AI should understand what we want and how to do it.

Over time, I realized this is not the ideal workflow.

Two Problems With “Just Dump the Whole Context”

1) The context is too long, too messy

The same point may be discussed, overturned, and rebuilt multiple times. That makes the AI confused about what the final version actually is, and it’s easy for implementation to drift off course.

2) We often don’t know what we don’t know

One of the hardest parts of using a coding AI agent today is that many people—especially those without a technical background—don’t truly know what a “complete system” includes.

Jumping straight from an idea to a “full implementation”, skipping PRDs, system design, architecture, and engineering documentation, and expecting the AI to write a system that satisfies everything from a fuzzy starting point is genuinely difficult.

What Abstraction AI Adds

Abstraction AI deliberately inserts a crucial step between “long context” and “actual development”:

It turns the context into a complete set of documents, and produces a clear design for the whole system.

This is a bit like manually inserting a deliberate “long thinking” step into the workflow—forcing a round of high-quality system-level thinking, structuring, and design before any code is written.

Flexible Inputs, Practical Outputs

In practice, the tool turned out to be very flexible. It can take:

• Any length of text
• AI chat logs
• Meeting transcripts
• A long project description you wrote yourself

No matter the user’s background, it generates a set of documents that you can hand to an AI engineer. With those documents, the AI can build the system more reliably, with higher success rates and more stable outcomes.

I intentionally made the documents beginner-friendly: usable for coding AIs, but also readable for people who aren’t very technical. Before you pay an AI to “do the work”, you can read the system description yourself, edit it according to your understanding, and then hand it off for implementation. After the docs are generated, you can also use a set of prompts I prepared to build a complete product directly from these documents.

Currently, it supports switching between GPT-5 and Gemini 2.5 Pro. The Gemini 2.5 Pro frontend visualization still has some rough edges, and I’ll keep improving it.

Cost, And an Unexpected Effect: Saving Money

The project itself was built with Augment Code, and the core prompt was written with help from GPT-5 Pro. End-to-end—building, iterating, debugging—the total cost was about $20.

Interestingly, this project was built by “having AI read long context”, not by starting with structured documentation. But my next startup project was implemented on top of the structured docs generated by this tool. That project was much more complex, closer to a complete system, and the total cost still came out to roughly $20.

That showed me a very direct effect: it saves money.

Because before execution, the AI already has a clear “instruction manual”. It can follow it, instead of repeatedly trial-and-erroring in ambiguous context and reworking mistakes.

And my next project is, in essence, also about making coding AI agents faster, better, and cheaper.

If you also tend to talk with AI for a long time before bringing in a coding AI, you might want to try converting “long context” into a complete, elegant, executable product document set first—then handing it off to your AI engineer.

This is my first time sharing a project publicly. If it helps anyone, that would mean a lot. Please try it, share it, and give feedback—those are incredibly valuable for someone like me who’s still learning how to work with users. Deploying on Builder Space was important for this project, and I’m grateful to the AI Architect course for making it so easy to share.

Comments

10 likes · 9 comments

mier20 (Full-Stack Engineer)

Thanks a lot for sharing—this is a great project. I’m curious: for users with a technical background, this can save some concrete implementation time. But for users without a technical background, how can they judge whether the AI-generated docs are correct, or whether they’re truly what the user needs?

Charlie

That’s a crucial question. What I tried to do inside the system—based on my experience prompting AIs to explain things—is to make the generated docs as friendly as possible to people of any background, so more people can actually read them. I also include a glossary to help with terminology.

So I think “help the user understand” is the first direction. The second direction is “learn with AI”: after downloading these docs, use a coding agent chat to ask it to explain things more clearly—ask wherever you don’t understand—until you feel confident you have a solid grasp.

If we’re using natural language to orchestrate compute, then helping users understand more of the natural language they didn’t understand before also expands the range of language they can use—so they can gradually obtain enough information to make the important judgments.

Charlie

Thanks for the kind words!

Xu Jia

I feel your tool solves the core problem of maximizing the effectiveness of collaboration between a person and AI tools. The efficiency improvement you mentioned is just the result. The deeper point is: your tool draws clear boundaries for the AI tool, and the AI tool explores and optimizes within those boundaries. I’d love to discuss further and learn from each other. Thank you very much for sharing.

Charlie

Thanks for trying it and for the feedback! The efficiency gain was indeed something I discovered unexpectedly—my main goal was still to help AI develop better things in a better way.

Your description—optimizing within boundaries—is very accurate and inspiring. For example, Claude Opus 4.5 will look back at the docs at the right time to check whether requirements are met and what tests might be missing. The development process shifts from “brute-force, messy exploration” to an optimization process with clear ground truth, a way to compute loss, and a path for backpropagation. After multiple iterations, it tends to converge to what we want.

Developing software like training an optimized model—this has been how AI coding has felt to me for a while, and this project really makes that path smoother.

Xu Jia

My experience is very similar to what you described. I’ve discussed a potential project for a long time with multiple LLMs, kept a large amount of text discussion records, and ended up generating multiple PRD versions—but the development process becomes more and more chaotic.

Charlie

Yeah—if you want to leverage different LLMs’ strengths, it can definitely lead to that kind of difficulty.

Xu Jia

Could I try it? How do I use it? Thanks.

Charlie

Here’s the link: https://abstractai.ai-builders.space/ Thanks for your interest!