Direct task description, role assignment, format specification.

Zero-Shot Prompting — Instruction Without Examples

Zero-shot prompting asks the model to perform a task using only the task description — no examples provided. Modern frontier models (GPT-4o, Claude Sonnet, Gemini Flash) can perform most common tasks zero-shot: classification, summarisation, translation, extraction, sentiment analysis. Zero-shot works well when: the task is clearly described, the model has seen similar tasks in training, and the output format is specified. Add examples (few-shot) when zero-shot fails consistently.

Interactive Pipeline Flow

UnderstandImplementOptimise

🧑 Role — persona setup

Role assignment: "You are an expert data analyst. You specialise in summarising complex reports into clear bullet points." Specific roles produce more consistent, expert-level outputs.

Architecture Diagrams

When to use zero-shot vs adding examples.

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Key Design Decisions

✅Zero-shot should be your default starting approach

Always try zero-shot first. Modern frontier models can handle most classification, extraction, summarisation, and translation tasks accurately without examples, saving tokens and simplifying prompt maintenance.

📐Format specification is the most impactful zero-shot improvement

Adding "Respond with ONLY: Positive/Negative/Neutral" to a sentiment prompt often improves machine-parseable accuracy more than adding examples. Format constraints are free and universally effective.

🧪Evaluate 30 examples before deciding zero-shot fails

Don't abandon zero-shot after 3 bad outputs. Evaluate 30 diverse examples, categorise failures, then decide: is the issue task ambiguity (fix description) or edge cases (add examples)?

Discussion