AI Support & Agents

Few-Shot Learning

Definition

Few-shot learning is when you guide an AI model to perform a task by including a small number of examples in the prompt, so it can follow the demonstrated pattern without any retraining.

Also known as: few-shot promptingin-context learningexample-based prompting

How it works

You show the model a few input-output pairs — for instance, several tickets already labeled with the right category or answered in your preferred tone — then give it a new input. The model infers the pattern from those examples and applies it, a behavior known as in-context learning because no weights are updated.

It sits between zero-shot learning (no examples) and full fine-tuning (retraining on a large dataset). Few-shot is fast to set up and easy to adjust: change the examples, change the behavior.

Why it matters for support

Few-shot prompting is the quickest way to make an AI agent match your team's voice and formatting. Feeding it a handful of your best real responses — often drawn from macros or resolved tickets — nudges it toward consistent, on-brand answers without a fine-tuning project.

Frequently asked

What is the difference between few-shot learning and fine-tuning?

Few-shot puts examples in the prompt at runtime, with no model changes — fast and flexible but limited by the context window. Fine-tuning retrains the model's weights on many examples, which is more durable but slower and costlier to set up.

How many examples is few-shot?

Typically a handful — often two to a few dozen, depending on the task and the context window. The goal is enough examples to demonstrate the pattern without crowding out the actual input.

Related terms

Zero-Shot Learning

Zero-shot learning is when an AI model performs a task it was never explicitly trained or given examples for, relying instead on the general knowledge it learned during pre-training and a clear description of the task..

Fine-Tuning

Fine-tuning is the process of further training a pre-trained language model on a smaller, task-specific dataset so it adapts to a particular domain, style, or behavior — updating the model's weights rather than just its instructions..

Prompt Engineering

Prompt engineering is the practice of designing and refining the instructions given to a language model to get accurate, relevant, and reliable outputs for a specific task..

Large Language Model (LLM)

A large language model (LLM) is a neural network trained on vast amounts of text to predict and generate language, enabling it to understand questions, summarize, classify, and write human-like responses..

System Prompt

A system prompt is the underlying set of instructions that defines an AI model's role, behavior, tone, and boundaries for a conversation — set by the developer, not the end user, and applied to every interaction..

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