As organizations integrate corporate data with Natural Language Models (NLMs), Retrieval-Augmented Generation (RAG) is essential for enhancing AI accuracy and relevance, especially for complex queries and unstructured data. RAG allows businesses to unlock insights while maintaining control over data access, privacy, and compliance. When choosing RAG solutions, organizations should consider scalability, performance, integration ease, security features like encryption, and cost efficiency to ensure the system meets their data needs and budget.

To help organizations navigate their choice in RAG adoption, BARC has prepared the research note: Why and How Retrieval-Augmented Generation Improves GenAI Outcomes.