Glossary

Zero-Shot Learning

What is 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.

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KI.ordo agents handle many tasks from their instructions alone; adding examples to the instructions can improve results in a targeted way.

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Topics

LLM
Prompting
Basics

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Related terms

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.

Prompting
Examples
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Transfer Learning

In transfer learning, a model that has already learnt a large general task serves as the starting point for a new, specialised one. Instead of starting from scratch, it is adapted, for instance through fine-tuning, using comparatively little data of your own. This saves computing time and data, for example when a general image model is meant to learn to spot production defects in photos.

Machine learning
Training
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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

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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