Overview:
Generative AI is transforming industries by providing personalized responses to user questions, hence fostering better customer experiences and making organizations more productive. However, a major challenge to using generative AI is ensuring that responses are accurate and brand appropriate.
LLMs that enable generative AI provide better user experience. An optimal way of improving LLMs’ accuracy is using Retrieval Augmented Generation (RAG). It combines elements of both text generation and information retrieval to enhance the quality and relevance of generated content.
RAG represents a significant advancement in generative AI, enabling models to produce factually grounded and reliable outputs across various tasks.
Super-powered research assistant for AI:
At its core, RAG is a sophisticated tool designed to pull relevant data from external sources and embed it into the foundational AI models such as ChatGPT and Bard. This integration enhances contextual understanding, thereby elevating the quality and richness of the ultimate output.
RAG works in two main stages: retrieval and generation. When you ask a question to an AI assistant, RAG first searches through an external knowledge base to find relevant information. This retrieval stage acts like a personal research assistant for the AI tool, gathering necessary knowledge to answer the question. Once the information in retrieved, it is fed to the AI, which then uses this information to craft its response.
Quality of answers produced by RAG depends on the quality of information that is placed in its knowledge base. Much of the dialogue on RAG centers is textual in its original format. However, companies are now producing more video and audio content, which will require special processing to extract metadata and transcripts that can be integrated in the database.
RAG offers extensive advantages, addressing issues such as hallucinations by filling gaps in the foundational AI models' knowledge, providing crucial context for precise responses. It also overcomes the time limitations of training data, granting the model access to current information post-training. An effective and efficient way to augment foundation models with domain-specific data by building Vector database, a type of database where a collection of data is stored as mathematical representations.
Market landscape:
Many companies are actively researching and developing RAG models, recognizing their potential to enhance the accuracy and reliability of AI applications.
Google is developing open-source RAG libraries and exploring its applications. Microsoft has launched GPT-RAG, an enterprise RAG solution accelerator that empowers businesses to harness the power of LLMs within their enterprise with unmatched security, scalability, and control. Amazon is exploring RAG’s potential in improving search results.
Salesforce Einstein Copilot Search will use the Data Cloud Vector Database to combine semantic search with traditional keywork search. The company is using RAG model in Sales and Service Cloud. IBM Research has incorporated RAG into their Watson Assistant platform, allowing businesses to build chatbots that access and leverage external knowledge for more accurate responses.
AI firms are working on this model too. Hugging Face is an open-source platform that offers readily available RAG models and libraries, helping developers to easily integrate RAG into their projects. Pincecone is a vector database that stores and retrieves high-dimensional data.