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AI glossary

Few-Shot Learning

Few-shot learning is the practice of guiding an AI by including a few examples in your prompt so it follows the pattern you want.

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In this guide

  1. 1What Few-Shot Learning means
  2. 2Why Few-Shot Learning matters

What Few-Shot Learning means

With few-shot prompting, you do not just describe a task - you also show the AI a small number of completed examples. The model picks up the pattern from those examples and applies it to your new input.

For example, to classify customer messages, you might include three sample messages each labeled with the right category, then add a new message. The AI follows the demonstrated pattern and labels it the same way.

Why Few-Shot Learning matters

Few-shot prompting is a simple, powerful way to make AI follow a precise format or style. It is one of the most practical prompting techniques you can learn.

Examples make AI output far more consistent
It is the easiest way to lock in a specific format
It needs no coding - just a few good examples
It is a core prompt engineering technique

Frequently asked questions

Often just two to five clear examples are enough. The goal is to show the pattern plainly - quality and consistency in your examples matter more than including a large number.

More AI terms

Chain-of-Thought PromptingA prompting technique that asks an AI to reason step by step, improving accuracy on complex tasks.Prompt ChainingBreaking a task into several prompts where each AI step feeds its output into the next.AI AgentSoftware that uses AI to pursue a goal on its own - deciding the steps and taking actions to get there.Agentic AIAI systems that act with autonomy - planning, deciding, and carrying out multi-step tasks toward a goal.Generative AIAI that creates new content - text, images, audio, or code - based on patterns learned from data.Artificial IntelligenceTechnology that lets computers perform tasks that normally require human intelligence, like understanding language.

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