Retrieval-Augmented Generation
Fully Realize the Value of Your Enterprise Content
Leverage retrieval-augmented generation (RAG) to search, retrieve and generate accurate information based on your enterprise content to accelerate innovation and improve customer experiences.
Improve the Accuracy, Relevance and Versatility of Natural Language Processing Tasks
By incorporating real-time data retrieval from your organization’s content repositories to enhance the performance of AI applications, like search engines and chatbots, RAG provides more accurate and contextually relevant responses to Natural Language Processing (NLP) tasks. This hybrid approach to customizing a Large Language Model (LLM) with data seamlessly integrates the capabilities of information retrieval and text generation. The result delivers better user experiences and may be particularly valuable when engaging customers in an advanced Question-Answering System or to enhance conversational agents and chatbots on your organization’s website. With RAG, organizations can take advantage of next-level content curation and enrichment, knowledge discovery and automation.
Improve Accuracy
RAG combines the benefits of retrieval-based and generative models, leading to more accurate and contextually relevant responses.
Enhance Contextual Understanding
By retrieving and incorporating relevant knowledge from a knowledge base, RAG demonstrates a deeper understanding of queries.
Reduces Bias and Misinformation
RAG’s reliance on verified knowledge sources helps mitigate bias and reduces the spread of misinformation compared to purely generative models.
Versatility
RAG can be applied to various NLP tasks, such as question answering, chatbots, and content generation.