In the age of information, organizations face the challenge of making sense of fragmented, unstructured data. RAG empowers entities to extract and synthesize knowledge effectively.
Retrieval-Augmented Generation combines information retrieval with natural language generation, enabling AI to access real-time, relevant information for accurate and context-rich responses.
Integrating RAG with knowledge graphs significantly boosts its power, allowing for deeper understanding through structured networks of entities and their relationships, thus enhancing reasoning and accuracy.
Traditional language models have limitations regarding up-to-date knowledge. RAG answers this via a retrieval module that connects to real-time data sources and generates informed responses.
Collection
[
|
...
]