// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
User Prompt
The specific instruction or question that a user gives to an AI model to get a desired response.
TECHNICAL DEFINITION
The explicit input text provided by a human user to an LLM, serving as the primary directive or query that guides the model's generation of a response, defining the task and context.
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 and prompt contexts supplied to the GenAI model, such as system instructions, metadata, API tools and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Prompt
- input query
- user query
- instruction
USAGE NOTE
Crafting effective user prompts is fundamental to achieving desired AI outputs.
DEVELOPERS
Organizations developing technology related to User Prompt.
Creator of GPT models and ChatGPT. Their research and products are centered on improving how AI interprets and responds to user prompts, offering APIs that are fundamental tools for prompt engineering.
Develops the Claude family of AI models with a focus on safety and reliability. Their research, including 'Constitutional AI', directly addresses how models should interpret and act upon user prompts to be helpful, harmless, and honest.
Develops large language models like Gemini. Products such as the Gemini app and Google's Search Generative Experience are entirely driven by user prompts, and their Vertex AI platform provides tools for developers to engineer prompt-based applications.
Provides large language models and APIs specifically for enterprise use. Their platform offers tools for developers to build and scale AI applications, which inherently involves the design, management, and optimization of user prompts.
An open-source framework for developing applications powered by language models. Its core functionality is built around creating 'chains' that structure and manage interactions with LLMs, making prompt templating, optimization, and management a central feature.
Provides data infrastructure for AI, specializing in generating and annotating data to train and evaluate models. They create high-quality prompt-response datasets for instruction tuning and Reinforcement Learning from Human Feedback (RLHF), which directly teaches models to better understand user prompts.
A company providing a dedicated toolset for prompt engineering and LLM application development. Their platform enables developers to experiment with, compare, and deploy different prompts across various models, streamlining the prompt design lifecycle.
A platform and community that provides essential tools, models, and datasets for machine learning. Their 'transformers' library and Inference Endpoints are critical infrastructure for developers building applications that process user prompts.