Glossary

Few-shot learning

What is Few-shot learning?

Few-shot learning is a model’s ability to solve a task from only a handful of examples. With language models this usually happens directly in the input: you show two or three pairs of question and desired answer, and the model applies the same format and style to new cases – without retraining. Example: three correctly categorised customer enquiries are often enough for further enquiries to be sorted the same way.

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In KI.ordo you can write examples directly into an agent’s instructions to set the tone and format of its answers.

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Topics

Prompting
Examples
Language models

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Related terms

Zero-Shot Learning

Zero-shot learning means that a model can solve a task without having seen specific examples of it beforehand. Large language models, for instance, can carry out a translation or classification based solely on a description in the prompt. If the quality is not sufficient, few-shot learning often helps: you add a few examples of the desired answer.

LLM
Prompting
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Prompt

A prompt is the input that tells an AI model what to do – a question, a task or an instruction, often supplemented with context, examples and the desired format. The quality of the answer depends heavily on how clear and complete the prompt is. For example, rather than “Write something about our product”, use “Write three short benefits of product X for trade businesses, in a factual tone”.

prompting
input
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Template

Metaprompt

A metaprompt is an instruction that asks a language model to draft or improve prompts or system instructions itself. You describe the goal, audience and desired tone and supply examples; the model then produces structured instructions with a role, rules and limits. This yields good first versions faster, which are then tested and refined by people. Example: “customer service for a bicycle shop” becomes a complete draft set of instructions.

prompting
agents
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Template

Large language model (LLM)

A large language model (LLM) is a neural network trained on very large amounts of text, enabling it to understand and generate language. It works by calculating the most likely next token, step by step. LLMs write, summarise, translate, answer questions and generate code; well-known examples include Claude, GPT, Gemini and Llama. Without access to current sources, they only know what was in their training data.

Language models
Generative AI
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