Designing AI Maintainable Code

Why This Post Exists #

AI tools are great at writing code, but they’re terrible at picking up where you left off. You paste in part of a script, ask for help, and the model fumbles because it doesn’t know what it’s looking at.

It’s not necessarily a model problem — it’s a context problem.

I started adding small hints to my projects to help the AI stay oriented: a few comments, a metadata file, a sentence or two at the top. The results were immediately better. Less wasted time. Smarter suggestions. Fewer “let me explain this again” moments.

Eventually, I realized this could be a pattern — one that’s simple, repeatable, and helpful across projects. In the future, it could even power IDE plugins and quality-oriented pipeline actions.

What I Mean by AI-Maintainable Code #

This isn’t about writing documentation for ChatGPT. It’s not about crafting prompts or retraining models. It’s just adding a layer of context that lives alongside your code — enough for an LLM to understand what’s happening without you having to spell it out every time.

Call it metadata. Call it signposting. The point is: you’re making the code more legible to a machine that doesn’t know the backstory.

The Core Pattern (The AI-Meta Layer) #

Here’s the basic structure I use:

These files are not just for the AI in the moment — they’re also friendly to embedding and vector search. If you’re using a retrieval-augmented (RAG) setup, you can index them to improve multi-file understanding and scoped responses.

This isn’t a heavy lift. Most files are 5–10 lines. Add what helps, ignore what doesn’t.

A Quick Example #

One of my scripts takes a Wikipedia article, summarizes it with an LLM, generates voiceover with say, and stitches it into a video with ffmpeg.

With no context, the AI kept guessing wrong about which part did what. After adding a one-liner comment at the top (# @ai: summarize → narrate → video), a short ai_context.yml, and a few purpose comments inline, it started suggesting useful edits instead of hallucinating new pipelines.

This isn’t magic. It’s just giving the AI a fighting chance.

Why This Helps #

• Fewer repeats – you don’t need to re-explain your project every time
• Better AI responses – edits are scoped to what you actually meant
• Scales well – other devs and future you benefit too
• No lock-in – this works with any LLM, anywhere

It’s a small effort with a big return — especially if you use AI tools regularly.

If You Want to Try It #

There’s a GitHub repo with the v0.1 spec and a starter template.

If you’re curious:

1. Add an @ai: line to one script header
2. Create a tiny ai_context.yml with the basic flow
3. Run a real AI session against it and see what changes

That’s enough to feel the difference.

Optional: Bootstrap Prompt for Your LLM #

In the future, tooling can grab a bootstrap prompt directly from something like ai_bootstrap_prompt.txt.

For now, just consider inviting your LLM to work with your code using a quick intro like this:

 This project follows the AI-maintainable code pattern. It includes structured context files and inline annotations (`@ai:`) to help you understand the project's purpose, flow, and key relationships. Use those to ground your reasoning before making changes or asking clarifying questions.

Final Thought #

At the end of the day, LLMs are just machines. If we want useful output, they need useful input.

Structure matters. Intent matters. And context — more than anything — matters.

… Huh. Maybe they’re like us, afterall. :)