Zero-Shot vs Few-Shot Prompting: When Each Technique Works

When Zero-Shot Is Sufficient

Zero-shot works well when the task is well-described in natural language, the desired behaviour is within the model's training distribution, and the output format is simple or standard. For tasks like: "Summarise this document in three bullet points," "Extract the invoice date from this text," or "Is this email spam? Answer yes or no" — clear zero-shot instructions often produce equivalent or better results than few-shot examples, with lower token cost and simpler maintenance.

— Tasks with clear natural language descriptions that the model understands without demonstration

— Common task types: summarisation, basic extraction, simple classification, translation

— When example quality is uncertain — bad examples hurt more than no examples

— When the output format is standard and the model is familiar with it

When Few-Shot Examples Are Worth the Cost

Few-shot examples add value when the task has domain-specific patterns the model is unlikely to infer from instructions alone, when the output format or style is non-standard and hard to describe precisely, or when zero-shot produces inconsistent results that examples can anchor.

— Domain-specific classification: your categories have specific meanings that differ from generic language (e.g., "escalate" means something specific in your support context)

— Specific output format: a structured format with unusual conventions that is easier to demonstrate than describe

— Style and tone: when examples demonstrate voice and register more precisely than instructions can

— Ambiguous boundary cases: examples can show how to handle the specific edge cases your task produces

Designing Effective Few-Shot Examples

— Use real examples from your actual data, not constructed ones — real examples represent the distribution and edge cases that matter

— Cover the boundary cases, not just the easy ones — examples that are clearly in one category do not help the model handle the hard ones

— Balance categories — if you have 3 categories, include examples of all 3 in roughly equal proportion

— Keep examples as similar in length and format to real inputs as possible

— Test the sensitivity of your output to example order — some models are sensitive to which example comes last

— Measure the quality improvement over zero-shot before committing to the added token cost

Chain-of-Thought: When Reasoning Matters

Chain-of-thought prompting asks the model to show its reasoning before providing a final answer. It significantly improves performance on tasks requiring multi-step reasoning: mathematical calculations, logical inference, complex classification that requires considering multiple factors. For simple classification or extraction tasks, chain-of-thought adds output length and cost without improving accuracy — use it specifically for reasoning-intensive tasks where the intermediate steps are required to reach the correct conclusion.