techniques

In-Context Learning (ICL)

LLMs learning to perform tasks from examples provided in the prompt without weight updates.

Definition

In-context learning is the ability of LLMs to learn from demonstrations provided within the input prompt.

Key Insight: The model learns the pattern from examples without any gradient updates - it's all inference-time learning.

How It Works: 1. Include task demonstrations in prompt 2. Model recognizes the pattern 3. Applies pattern to new input

Factors Affecting Performance: - Example quality and relevance - Example ordering - Format consistency - Model size (emergent in large models)

Research Findings: - Label correctness matters less than format - More examples generally help - Example diversity important

Including example Q&A pairs before your actual question to guide the model's response format.

The practice of designing effective prompts to get better outputs from AI models.

AI ability to learn new tasks from just a handful of examples.

AI ability to perform tasks it was not explicitly trained on.

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