Eli5 Your AI: Beating the Context Window Problem in AI-Assisted Development
When you work with human developers, you already know the value of a good project brief. The more precise you are, the fewer misunderstandings, rewrites, and dead-ends you face.
The same is true when working with AI tools for coding — except there’s a catch: the context window problem.
What’s a Context Window, and Why It Trips People Up
A “context window” is the amount of information an AI model can “remember” at any one time. It’s not memory in the human sense — it’s more like the whiteboard space in a meeting room. Once it’s full, new information starts pushing old information off the board.
Some modern AI models can handle huge context windows — tens of thousands of words — but no model can remember an entire evolving project forever.
That means if you’ve been working with AI over multiple prompts or days, you can’t assume it still “knows” your entire codebase, all the requirements, or all the decisions you’ve made along the way. If you keep going without re-establishing the context, you risk the AI:
Making decisions based on outdated info
Forgetting your chosen architecture
Reintroducing bugs you already fixed
Inventing functionality that doesn’t exist
In short, it’s like asking a new developer to take over mid-project with zero handover.
Why This Feels Worse Than With Humans
With a human developer, you can rely on them to remember project history, even if you haven’t reminded them of every detail. With AI, that’s not the case.
The AI doesn’t “know” your app unless you tell it about your app — every time you need it to work with the whole picture.
The Developer Brief Analogy
If you dropped a junior developer into a new project, you wouldn’t just say “fix the bug.” You’d give them:
An overview of the app
The key files or components involved
The environment setup
The known problems and constraints
You’d also answer their questions, check their understanding, and only then set them loose.
You have to treat AI the same way.
The Cliffs Notes Strategy
The simplest way to avoid context loss problems is to give your AI a lightweight “Cliffs Notes” version of your product at the start of every coding session.
This might include:
What the app does – high-level description in plain English
Tech stack – languages, frameworks, databases
Architecture – how the main parts fit together
Key quirks or constraints – performance concerns, platform limits, style preferences
Your current task – exactly what you want changed or built
Even better, ask the AI to produce its own “Eli5” version of your product description so you can check its understanding before it writes a single line of code. If it’s wrong, correct it — otherwise, you’re building on sand.
The “Treat It Like a New Hire” Rule
Whenever you open a new chat, change AI tools, or return to a project after a break:
Reintroduce the product — the short version, not the whole codebase.
Provide key files or excerpts — just the bits relevant to the task.
Check its understanding — get it to explain the product back to you like you’re 5 years old.
Then and only then… ask for the code changes you need.
Why This Works
Following this discipline:
Prevents AI from hallucinating features.
Keeps architecture decisions consistent.
Speeds up iteration, because you’re both working from the same mental model.
So don’t just prompt — onboard your AI like it’s the newest (and forgetful) developer on the team.
