// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Retrieval Augmented
Describes AI models or systems that enhance their ability to generate responses by first looking up relevant information from an external source.
TECHNICAL DEFINITION
Retrieval Augmented refers to a class of AI architectures or techniques, prominently Retrieval Augmented Generation (RAG), where a model's generative capabilities are enhanced by integrating an explicit information retrieval component that fetches relevant external knowledge to inform and ground the generation process.
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
- Knowledge-Enhanced
- Externally Grounded
- Information-Informed Generation
USAGE NOTE
Retrieval augmented systems are becoming standard for applications requiring factual accuracy and up-to-date information.
DEVELOPERS
Organizations developing technology related to Retrieval Augmented.
A framework designed to simplify the creation of applications using large language models, providing tools for retrieval-augmented generation (RAG) to connect LLMs with external data sources.
A data framework for LLM applications that provides tools to ingest, structure, and access private or domain-specific data, making it central to building RAG systems.
An AI community and platform that provides tools, models, and datasets, including resources and implementations for various RAG architectures and models.
A vector database for building high-performance vector search applications, which serves as a critical component for storing and retrieving embeddings in RAG systems.
An open-source vector database that allows for efficient storage and retrieval of data objects by vector embeddings, foundational for RAG implementations.
Conducts extensive research in AI and develops LLMs, often incorporating retrieval mechanisms to ground models with up-to-date or domain-specific information.
Develops and open-sources various AI models and research, including contributions to retrieval-augmented models and techniques for improving LLM factual consistency.
Provides enterprise-grade large language models and a platform that supports grounded generation, which is a key aspect of retrieval-augmented approaches.
Offers a data intelligence platform that enables enterprises to build and deploy AI applications, including leveraging RAG for custom LLM solutions.