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 Prompt
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