Zero-Shot and Few-Shot Prompting: Control LLM Behavior Without Fine-Tuning

Zero-Shot Prompting

Zero-shot means giving the model no examples — just a clear instruction. Modern LLMs handle zero-shot well for standard tasks.

Classify the following text as Technical, Business, or General.

Text: "The new transformer architecture reduces inference latency by 40%."

Output: Technical

Zero-shot works when:

• The task is well-defined and common
• The output format is standard
• You need speed and simplicity

Few-Shot Prompting

Few-shot adds 2–5 examples before the actual input. The model learns the pattern and applies it.

Classify each text. Reply with only the label.

Text: "Q3 revenue up 12% YoY" → Business
Text: "Updated the CI/CD pipeline" → Technical
Text: "The weather is nice today" → General

Text: "Deployed new containerization strategy to reduce costs" →

Output: Business or Technical — the model will infer from context.

When to Use Each

How Many Examples?

• 3 examples: usually sufficient for simple classification
• 5 examples: good for nuanced tasks
• 8–10 examples: complex tasks or rare edge cases
• More than 10: rarely helps and increases cost

Always include at least one example of each class you want the model to produce.