Skill

Few-Shot Learning

Data Science, Analytics and AI/ML

Few-shot learning is a machine learning approach, particularly prominent with large language models, where a model performs a new task after being given only a handful of examples in its input prompt, rather than requiring extensive retraining. It leverages the general knowledge a pretrained model already has, applying it to novel tasks with minimal additional data—contrasted with zero-shot (no examples) and traditional supervised learning (many examples). AI researchers and engineers use it to quickly adapt models like GPT or Claude to specialized tasks without fine-tuning.

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• Fine-tuning
• Prompt Engineering
• Transfer Learning