Technique Aktualisiert 2026-04

Zero-shot

Zero-shot Learning
Definition

Zero-shot enables a model to accomplish a task without any prior examples, solely from the instruction.

Siehe auch im Glossar

F
Few-shot
Few-shot involves providing a few examples in the prompt to guide the model toward the desired response format or style.
P
Prompt
A prompt is the instruction or question you give an AI to get a response. It's the interface between you and the model.
L
LLM (Large Language Model)
An LLM is an AI model trained on billions of texts, capable of understanding and generating human language.
F
Foundation Model
A foundation model is a large AI model pre-trained on massive data, adaptable to multiple tasks.

Tools, die zero-shot verwenden

C
ChatGPT
Der weltweit meistgenutzte KI-Konversationsassistent
4.6/5
C
Claude
Die KI, die Nuancen versteht – von Anthropic
4.7/5
G
Gemini
Googles KI-Assistent mit 1-Million-Token-Kontext
4.5/5
D
DeepSeek
Das chinesische Open-Source-Modell auf GPT-4-Niveau
4.7/5

Häufig gestellte Fragen

What's the difference between zero-shot and few-shot?
Zero-shot: no examples provided, the model understands the instruction alone. Few-shot: 2-5 examples are provided to guide the model. Few-shot often gives better results.
Can all LLMs do zero-shot?
Large LLMs (GPT-4, Claude, Gemini) excel at zero-shot. Smaller models often need examples (few-shot) to perform well.