Few-Shot Learning
A technique where models learn to perform tasks from just a few examples provided in the prompt, without additional training.
In-Depth Explanation
Few-shot learning enables AI models to adapt to new tasks by seeing just a handful of examples in the prompt. Instead of requiring thousands of training examples, modern LLMs can learn patterns from 2-5 demonstrations.
How few-shot learning works:
- Include examples of the desired input-output pattern in your prompt
- The model recognises the pattern from the examples
- It applies the same pattern to your actual query
- Outputs follow the demonstrated format and logic
The term comes from the number of examples:
- Zero-shot: No examples, just task description
- One-shot: Single example provided
- Few-shot: 2-10 examples provided
Few-shot learning is powerful because:
- No model training or fine-tuning required
- Instantly adaptable to new tasks
- Easy to iterate and improve
- Works across diverse domains
- Examples serve as implicit instructions
Research shows that quality of examples matters more than quantity - well-chosen, diverse examples outperform many similar ones.
Business Context
Few-shot learning lets you customise AI behaviour instantly by showing examples, avoiding the time and cost of model training.
How Clever Ops Uses This
Few-shot prompting is our go-to technique for rapid prototyping with Australian business clients. We can demonstrate AI capabilities within hours using carefully crafted examples.
Example Use Case
"Including 3 example customer support responses in your prompt to guide the AI's tone, format, and content approach."