AI Glossarytechniques

Few-Shot Learning

A prompting technique where you provide a small number of input-output examples in the prompt to teach the model the desired behavior.

How It Works

Few-shot learning is one of the most effective prompt engineering techniques. Instead of describing what you want in abstract terms, you show the model 2-5 concrete examples of the desired input-output pattern. The model generalizes from these examples and applies the pattern to new inputs. For example, to classify customer feedback sentiment, you include: "Great product, love it! -> positive", "Terrible experience, want a refund -> negative", "It works fine, nothing special -> neutral" in your prompt. The model then correctly classifies new feedback without any fine-tuning. Few-shot is the sweet spot between zero-shot (no examples, less reliable) and fine-tuning (expensive, requires training data). Use it when: (1) the task has a specific output format, (2) the model struggles with zero-shot accuracy, (3) you need consistent formatting across responses. Most production AI features use few-shot prompting for data extraction, classification, and structured generation tasks.

Common Use Cases

  • 1Data extraction with specific formats
  • 2Text classification
  • 3Style matching and tone control
  • 4Structured output generation
  • 5Custom entity recognition

Related Terms

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

Prompt Engineering

The practice of crafting effective instructions for AI models to produce desired outputs consistently.

Chain of Thought (CoT)

A prompting technique that improves AI reasoning by instructing the model to break down complex problems into intermediate steps before giving a final answer.

Zero-Shot Learning

The ability of an AI model to perform a task based solely on instructions, without any training examples provided in the prompt.

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