Few-Shot Learning.

Few-shot learning enables AI models to perform new tasks given only a handful of examples, rather than requiring large labelled datasets. In the context of prompting, few-shot means including 2-5 examples of the desired input-output format in the prompt itself, allowing the model to infer the pattern and apply it to new inputs.

Comparison to Other Learning Approaches

Zero-shot learning gives the model no examples and relies on its pre-trained knowledge. Few-shot learning provides 2-10 examples. Many-shot learning provides more. For most marketing use cases—content formatting, brand voice replication, structured data extraction—few-shot prompting strikes the right balance between effort and performance.

Practical Use Cases

Marketers use few-shot learning to replicate specific brand voice in AI-generated content, format outputs consistently (like JSON, CSV, or markdown), extract structured data from unstructured text, and categorise content at scale. Providing 3-5 high-quality examples is often more effective than writing elaborate instructions.