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Plain-language AI glossary

Term 23TechniquesMeaning / context / connections

Techniques / Definition

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.

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MeaningThe one-sentence definition.

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01 / Plain-language context

How Few-Shot Learning 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.

02 / Practical uses

Where it helps.

  1. 01Data extraction with specific formats
  2. 02Text classification
  3. 03Style matching and tone control
  4. 04Structured output generation
  5. 05Custom entity recognition
03 / Keep exploring
Prompt EngineeringThe practice of crafting effective instructions for AI models to produce desired outputs consistently.Zero-Shot LearningThe ability of an AI model to perform a task based solely on instructions, without any training examples provided in the prompt.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.Fine-TuningThe process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.