Fine-Tuning vs. Context Injection
Why teaching your AI "new facts" via training is usually the wrong move.
There is a common pattern happening in engineering teams right now: Using Fine-Tuning when you actually need Context.
These are two different tools.
The Analogy: University vs. Cheat Sheet
Fine-Tuning is like University. You send a model to school for a week to learn your coding style.
- Pros: It deeply internalizes your general "vibe".
- Cons: It takes days, costs money, and the moment it graduates, your codebase has already changed.
Context (CorePackAI) is a Cheat Sheet. You hand the model a document right before it starts working.
- Pros: It's instant, free, and perfectly accurate to the current second.
- Cons: None.
The Problem with "Baked" Knowledge
If you fine-tune on your repo, you are baking knowledge into the model's weights.
But codebases are volatile. If you rename Buttton to AppButton today, your fine-tuned model (trained last week) will confidently hallucinate Button. It doesn't know about the rename yet.
To fix this, you have to re-train. That is a heavy operational loop.
The Pragmatic Solution
Instead of training, just inject the rules.
With CorePackAI, you define a simple markdown file:
# Design System Rules
1. COLORS: Use `var(--brand-primary)`, not hex codes.
2. COMPONENTS: Use `<AppButton>`, not `<button>`.
When you run npx corepackai install, this file is bridged directly to your agent (Cursor, Antigravity, Copilot).
The model reads it and follows it instantly. No training required.
The Future: Installable Intelligence
This isn't just about fixing CSS. It's about a fundamental shift in how we build software.
For the last 15 years, we have had Installable Code (npm install react). You don't write a UI framework from scratch; you install one.
We believe the next decade is about Installable Intelligence.
Imagine if, instead of prompting your agent from scratch every time, you could simply install a skill:
- Need a Security Engineer?
npx corepackai install @security/auditor - Need a Stripe Expert?
npx corepackai install @stripe/integration-guide
This decouples the Model (the raw brain power) from the Context (the specialized knowledge).
It means you can swap out models (GPT-4 to Claude 3.5) without losing your project's IQ. It means knowledge becomes modular, versioned, and shared across the community.
Summary
- Fine-Tune for Skills (New languages, specific formats).
- Context for Facts (Your APIs, Your Architecture).
We are building the registry for this new world.
Explore the Vision
If this future of modular, composable intelligence makes sense to you, come see what we're building.