# Zero-Shot Prompting

> Zero-shot prompting asks a model to perform a task from instructions alone, with no examples — the default mode for capable modern LLMs.

**Zero-shot prompting is instructing a model to do a task with no demonstrations — just the description: "Classify this ticket as billing, bug, or feature-request. Respond with the label only."**

The term comes from the ML literature (performing a task with zero training examples), and its practicality is a product of instruction tuning: models are explicitly trained to follow natural-language task descriptions, so clear instructions alone now cover most well-defined work. That makes zero-shot the sensible *starting point* — cheapest in tokens, easiest to maintain, no example set to curate or go stale.

Its limits define when to escalate: when output must match a pattern easier shown than told, add [few-shot examples](/glossary/few-shot-prompting); when the task needs visible multi-step reasoning on a non-reasoning model, add [chain-of-thought](/glossary/chain-of-thought); when code consumes the output, enforce [structure](/glossary/structured-output) rather than describing it. The escalation path — and when each step actually pays — is the subject of [Few-Shot vs Chain-of-Thought vs Structured Prompting](/guides/prompting/prompting-techniques-2026).

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_Source: https://agentscamp.com/glossary/zero-shot-prompting — Term on AgentsCamp._