ML Research analyst || Vision Transformer Engineer ||AI Multi Agents Enthusiast||Data Scientist
"Retrieval Augmented Generation: Everything You Need to Know" Retrieval Augmented Generation (RAG) is an emerging methodology for building Generative AI applications, particularly useful for enterprises looking to utilize private or custom datasets. RAG enhances the capabilities of large language models (LLMs) by integrating them with additional factual data from specific sources, addressing the limitations of LLMs that may struggle with data outside their training sets, leading to inaccuracies or "hallucinations" in responses. The RAG process involves data ingestion, chunking and embedding, query processing, response generation, and optional validation. Key advantages of RAG include reduction of hallucinations, cost-effectiveness, explainability, and enterprise readiness. Vectara offers RAG as a managed service, simplifying the development and deployment of GenAI applications while handling complexities and ensuring enterprise-grade security and performance. RAG is becoming the standard framework for implementing enterprise applications powered by LLMs, providing a robust solution for leveraging custom data effectively while minimizing risks associated with traditional LLM usage. For more details, refer to the full article here:https://lnkd.in/gHKkFeWE
