In-Context Learning

Overview

In-context learning enables large language models to learn from examples provided within the prompt itself, without requiring model retraining. This approach allows models to adapt to new tasks by understanding patterns from provided examples.

Learning Process

The model learns by:

• Analyzing example patterns
• Identifying relevant features
• Recognizing task structure
• Applying learned patterns
• Generating appropriate responses
• Maintaining consistency

Implementation Methods

Effective in-context learning requires:

• Clear example selection
• Proper example formatting
• Task descriptions
• Context boundaries
• Pattern demonstration
• Quality verification

Common Applications

This approach supports:

• Document formatting
• Data extraction
• Classification tasks
• Text generation
• Query responses
• Pattern matching

Related Concepts

• Large Language Model
• Few-Shot Learning
• Prompt Engineering
• Context
• Prompt Templates