// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Few-Shot Prompt
A prompt that includes a small number of examples (e.g., 2-5) to guide the model on how to perform a task, without needing to retrain it.
TECHNICAL DEFINITION
A few-shot prompt leverages in-context learning by providing a limited set of input-output examples within the prompt itself, enabling large language models (LLMs) to generalize to new, similar tasks without parameter updates.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Example-based prompt
- In-context examples
- Few-example prompt
USAGE NOTE
Few-shot prompting is effective for quickly adapting LLMs to new tasks with minimal data.
DEVELOPERS
Organizations developing technology related to Few-Shot Prompt.
Develops leading large language models (LLMs) like GPT-3 and GPT-4, where few-shot prompting is a fundamental technique for guiding model behavior and enabling in-context learning without extensive fine-tuning.
Engaged in extensive research and development of foundational large language models such as PaLM and Gemini, which inherently rely on prompt engineering techniques like few-shot prompting for effective performance across a wide range of tasks.
Creator of frontier AI models like Claude, which are designed with a strong emphasis on robust prompt engineering, including the use of few-shot examples, to ensure safety, steerability, and high-quality task execution.
Provides comprehensive AI services and platforms, integrating advanced LLMs and offering tools that guide developers in implementing effective prompt engineering strategies, including best practices for leveraging few-shot prompts.
Offers a vast ecosystem of open-source models, datasets, and tools that facilitate the development and deployment of machine learning applications, enabling researchers and developers to experiment with and implement few-shot prompting for various NLP tasks.
Specializes in building large language models for enterprise applications, providing platforms and APIs that prioritize prompt design, where few-shot learning is a critical component for customizing model responses and behavior for specific business needs.
Develops an open-source framework designed to simplify the development of applications powered by large language models, offering modules and abstractions for prompt management, chaining, and integrating few-shot examples to enhance model interaction and capabilities.