In-Context Learning

In-context learning is why AI can adapt to your specific needs without training a custom model. It's the mechanism behind few-shot prompting's effectiveness.

How It Works

1. You provide examples — Input-output pairs in your prompt
2. Model identifies patterns — Extracts the transformation logic
3. Model applies patterns — Uses learned rules on new inputs

All of this happens within a single prompt — no model updates occur.

Why This Matters

Traditional machine learning requires:

• Collecting training data
• Running training processes
• Deploying updated models

In-context learning requires:

• Writing good examples in your prompt
• That's it

What AI Can Learn In-Context

• Formatting conventions — How to style code, messages, docs
• Domain terminology — Your project's specific vocabulary
• Transformation patterns — How to convert X to Y
• Coding style — Your team's preferred patterns

Limitations

In-context learning is powerful but not unlimited:

• Context window bounds — Examples take up space
• Complex patterns — May need more examples than fit
• Persistence — Learning doesn't carry between sessions

For persistent learning, combine in-context examples with Cursor Rules or system prompts.