In-Context Learning & Few-Shot Prompting - Interactive Visualization

In-context learning lets LLMs adapt to new tasks at inference time by including examples in the prompt - no weight updates needed. Adding even 1-5 demonstrations shifts the output distribution toward the desired behavior, with diminishing returns beyond ~8 shots. This demo shows how accuracy improves as you add more examples.

• Zero-shot vs few-shot: see the accuracy gap when zero examples are provided versus 1, 2, 4, and 8
• Diminishing returns curve: accuracy gains shrink as shot count increases beyond 4-8 examples
• Output distribution shift: watch how the model prediction changes as demonstrations accumulate
• Task adaptation at inference time: no gradient updates or fine-tuning required

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