// GLOSSARY -- AGENTIC AI

What is Zero-Shot Learning?

1 min read Updated

Zero-shot learning is an LLM's ability to perform a task with only instructions and no examples — relying entirely on the model's pre-trained knowledge and instruction-following capabilities.

WHY IT MATTERS

Zero-shot is the most convenient prompting approach: just describe what you want. Modern LLMs are remarkably good at following zero-shot instructions for: classification, summarization, translation, code generation, and many other tasks.

Zero-shot performance depends heavily on: model quality (larger models are better at zero-shot), instruction clarity, and task familiarity (tasks similar to training data work better).

Zero-shot is the default starting point for any LLM application. If zero-shot isn't good enough, add examples (few-shot). If few-shot isn't enough, consider fine-tuning.

FREQUENTLY ASKED QUESTIONS

When is zero-shot sufficient?
For standard NLP tasks (summarization, translation, classification), well-understood formats, and when using frontier models. The better the model, the more tasks it handles zero-shot.
How to improve zero-shot performance?
Write clearer instructions, specify output format, provide context about the task, and use chain-of-thought prompting for complex reasoning.
Zero-shot vs few-shot vs fine-tuning?
Zero-shot: fastest to implement, lowest consistency. Few-shot: moderate effort, good consistency. Fine-tuning: highest effort, best consistency. Progress through them as needed.

FURTHER READING

Enforce policies on every tool call

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npx -y @policylayer/intercept
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