// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Sample
A smaller, representative group taken from a larger collection of data.
TECHNICAL DEFINITION
A subset of a population or dataset, selected to represent the characteristics of the entire dataset for analysis or model training, often used to manage computational resources or when full data access is impractical.
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
- Subset
- data sample
- data subset
- observation
- instance
USAGE NOTE
Researchers often take a sample of user data to test new features.
DEVELOPERS
Organizations developing technology related to Sample.
A leading AI research and deployment company that develops large language models (LLMs) like GPT-4, making prompt engineering a critical skill for users and developers leveraging their APIs.
An AI safety and research company known for developing LLMs such as Claude, which emphasizes prompt design for responsible and controllable AI behavior through 'constitutional AI' principles.
A global leader in AI research and development, creating advanced AI systems including LLMs like Gemini, where sophisticated prompt engineering is essential for achieving desired outputs and mitigating biases.
Offers a comprehensive suite of AI services, including tools and frameworks within Azure AI Studio and Azure Machine Learning that support the development, deployment, and optimization of LLM-based applications, often featuring dedicated prompt flow and prompt engineering capabilities.
A platform and community that provides open-source tools, models, and datasets for machine learning, including extensive resources and libraries that facilitate experimentation, deployment, and sharing of prompt engineering techniques for various LLMs.
An open-source framework designed to help developers build applications with large language models. It provides abstractions and tools specifically for prompt management, chaining prompts together, and integrating LLMs with other data sources and tools, making it central to AI engineering and prompt design.
A data framework for LLM applications that focuses on ingesting, structuring, and accessing private or domain-specific data to augment LLMs. It works closely with prompt engineering to improve the context and relevance of model outputs, especially in retrieval-augmented generation (RAG) scenarios.
An MLOps platform that provides tools for tracking, visualizing, and organizing machine learning experiments. It is widely used by AI engineers to manage and compare different prompt designs and LLM fine-tuning runs, optimizing model performance and reliability.
Provides data annotation, data curation, and model evaluation services for AI development. They offer solutions for fine-tuning LLMs and optimizing prompt strategies, assisting companies in getting high-quality data and effective prompts for their AI applications.