// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Prompt Engineering
The art and science of designing and refining inputs (prompts) for AI models to achieve desired outputs, often involving iterative testing and optimization.
TECHNICAL DEFINITION
Prompt Engineering is the discipline of developing, optimizing, and refining input prompts for large language models (LLMs) to effectively steer their behavior, improve output quality, and unlock specific capabilities for various downstream tasks.
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 Crafting
- Prompt Design
- Prompt Optimization
- AI Instruction Design
USAGE NOTE
It's a critical skill for maximizing the utility and performance of generative AI systems.
DEVELOPERS
Organizations developing technology related to Prompt Engineering.
Pioneer in AI research and development, creating models like GPT-4, where effective prompt engineering is essential for optimal performance and application of their large language models.
Conducts extensive research in AI and develops large language models (e.g., Gemini), providing guidelines and tools for effective prompt engineering to leverage their models.
Develops advanced AI systems, including Claude, with a focus on AI safety and robust prompting techniques, including their 'Constitutional AI' approach that relies on structured prompting.
Provides a platform for building, training, and deploying machine learning models, offering tools and resources that facilitate experimentation and optimization of prompts for various language models.
An open-source framework designed to simplify the development of applications powered by large language models, including robust tools for prompt management, templating, and chaining.
Offers enterprise-grade large language models and a platform that includes tools and APIs for prompt engineering, fine-tuning, and deploying custom NLP applications.
Actively researches and integrates AI technologies across its products and services, providing platforms and guidelines for prompt engineering within Azure OpenAI Service and other AI tools.
Provides a data framework for building LLM applications, enabling users to connect their custom data sources to LLMs and design prompts for effective querying and information retrieval.