Retrieval augmented generation (RAG) is a natural language processing (NLP) technique that combines the strengths of both retrieval- and generative-based artificial intelligence (AI) models. RAG AI can deliver accurate results that make the most of pre-existing knowledge but can also process and consolidate that knowledge to create unique, context-aware answers, instructions, or explanations in human-like language rather than just summarizing the retrieved data. RAG AI is different from generative AI in that it is a superset of generative AI. RAG combines the strengths of both generative AI and retrieval AI. RAG is also different from cognitive AI, which mimics the way the human brain works to get its results.
RAG, short for retrieval augmented generation, works by integrating retrieval-based techniques with generative-based AI models. Retrieval-based models excel at extracting information from pre-existing online sources like newspaper articles, databases, blogs, and other knowledge repositories such as Wikipedia or even internal databases. However, such models cannot produce original or unique responses. Alternatively, generative models can generate original responses that are appropriate within the context of what is being asked, but can find it difficult to maintain strict accuracy. To overcome these relative weaknesses in existing models, RAG was developed to combine their respective strengths and minimize their drawbacks. In a RAG-based AI system, a retrieval model is used to find relevant information from existing information sources while the generative model takes the retrieved information, synthesizes all the data, and shapes it into a coherent and contextually appropriate response.
By integrating retrieval and generative artificial intelligence (AI) models, RAG delivers responses that are more accurate, relevant, and original while also sounding like they came from humans. That’s because RAG models can understand the context of queries and generate fresh and unique replies by combining the best of both models. By doing this, RAG models are:
These are some real-life examples of how RAG models are being used today to:
Cohesity is at the forefront in the dawning age of AI because the Cohesity platform is ‘AI Ready’ for RAG-based large language models (LLM). The ground-breaking Cohesity approach provides robust and domain-specific context to RAG-driven AI systems by leveraging the robust file system of the Cohesity patented SnapTree and SpanFS architectures. To achieve this, an on-demand index of embeddings will be provided just-in-time to the AI application requesting the data. Additionally, the data will be secured through Cohesity’s role-based access control (RBAC) models.
The Cohesity RAG platform currently under development accepts both human or machine-driven input such as questions and queries. That input is then tokenized with keywords that quickly filter petabytes of enterprise backup data down to a smaller subset of contextualized data. It then selects representations from within those documents or objects that are most relevant to the question or query. That result is packaged, along with the original query, to an LLM such as GPT4 to provide a context-aware and human-sounding answer. This approach is innovative and ensures that the generated responses are not only knowledgeable and up-to-date, but also diverse and relevant to the specific business content.
By layering RAG on top of an enterprise’s own datasets, Cohesity customers will not need to perform costly fine-tuning or extended training on vast volumes of data to teach LLMs “what to say.” This saves time and money and also reduces environmental impact, since RAG models are flexible enough to adapt to datasets that are rapidly growing and constantly changing. For this reason, leveraging RAG on the Cohesity platform can provide the most recent and relevant context to any query.
Cohesity’s RAG-aware platform will generate more knowledgeable, diverse, and relevant responses compared to off-the-shelf LLMs without massively increasing data storage requirements. This breakthrough has tremendous potential for new innovations with enterprise Q&A (questions and answers) applications and industry search and discovery models.
Technology and business executives alike will have a unique opportunity to leverage the power of data-driven insights to enhance the quality of AI-driven conversations with Cohesity’s RAG-driven AI system. By harnessing the power of Cohesity data management and security solutions, enhanced by AI, organizations can unleash new levels of efficiency, innovation, and growth.