In this video, our co-founder and CTO, Yannic Kilcher, delves into the increasingly discussed concept of Retrieval Augmented Generation (RAG). But what exactly does RAG entail, and how does it transform the capabilities of generative AI?
Generative AI and large language models (LLMs) are at the forefront of today’s workplace technologies, offering immense transformative potential. While LLMs excel in crafting coherent, human-like text, they often stumble in a critical area – knowledge retrieval, leading to the risk of serving incorrect information, or "hallucinations." This is not surprising as LLMs are statistical models that were not designed for the purposes of retrieving factual knowledge read more about LLMs here.
To combat this challenge, Retrieval Augmented Generation (RAG) introduces a pipeline framework that effectively decouples knowledge retrieval from the generation process by pairing an LLM with a powerful search engine. This approach empowers users to maximize the utility of LLMs, ensuring responses are consistently of high quality and reliability. A major benefit of RAG lies in its ability to incorporate new data points that may not have been present during model training, including private or post-training data. Grounded in regularly updated enterprise knowledge, RAG ensures responses are tailored to meet the demands of recency, relevancy, and permissioning rules, fostering informed decision-making and enhancing overall efficiency.
However, is it important to underscore the complexity of the RAG process and the importance of data quality and relevance. As Yannic asserts, “Good retrieval is paramount to good RAG”. Indeed, effective retrieval of pertinent references is foundational to the success of RAG implementation.
DeepJudge provides an intent-based search that covers the entire breadth of your organization, ensuring safe and secure integration while upholding access permissions – the most scalable and secure foundation for any firm-wide RAG-based applications.
