Zero-Shot Prompting

Zero-shot prompting is the default way most users interact with LLMs: you describe the task, the model produces output. No examples are included in the prompt.

Frontier models in 2026 are good enough at following instructions that zero-shot often beats few-shot for well-defined tasks with clear grading criteria. The more your instruction explicitly constrains the output shape, the less you need examples.

Example

Classify the following customer review as one of: positive, negative, neutral. Return only the label, nothing else.

Review: "Shipping took forever but the product is great."

When to use it

• The task is well-known (classification, summarization, translation)
• You have strong instruction clarity
• You are targeting a frontier model (GPT-5, Claude 4, Gemini 3)
• You need the prompt to stay short for cost/latency reasons

When not to use it

• The task is domain-specific and the model doesn't know your terminology
• Output format is unusual or has strict constraints examples would clarify
• You need consistent style the model can't infer