A RAG (Retrieval-Augmented Generation) application combines the power of natural language generation with real-time information retrieval to produce more accurate and contextually relevant responses. Unlike traditional language models that rely solely on pre-trained knowledge, a RAG system augments its output by dynamically retrieving relevant documents or data from external sources—such as databases, document stores, or APIs—at inference time.

This two-step process involves:

Retrieval: The system uses a query to search a knowledge base or vector store and retrieve the most relevant chunks of information.
Generation: It then feeds both the original query and the retrieved context into a generative language model to produce a final response.

Key Features:

Improves accuracy by grounding responses in up-to-date, factual information.
Enables scalable Q&A and summarization over large unstructured datasets.
Supports enterprise and domain-specific use cases (e.g., legal, healthcare, internal documentation).

Comsys RAG AppCOMSYS S.A.

Comsys RAG App

COMSYS S.A.

Comsys RAG App

COMSYS S.A.

Comsys RAG App
COMSYS S.A.