RAG Explained | Using Retrieval-Augmented Generation to Build Semantic Search

Large language models (LLMs) have gained significant attention since the launch of OpenAI's ChatGPT in 2022, prompting businesses to explore their practical applications. As more LLMs become open-source and deployable on-premise, organizations can customize these models using techniques like retrieval-augmented generation (RAG), which enhances model output accuracy by integrating pre-fetched data from external sources. RAG enables general-purpose LLMs to provide context-specific answers without the need for costly and complex custom model training. It involves embedding data into a vector database and retrieving relevant information during queries, thus reducing inaccuracies and ensuring up-to-date, reliable responses. Platforms like Orkes Conductor facilitate the orchestration of RAG systems by simplifying the interaction between data sources, vector databases, and LLMs, allowing for efficient and scalable deployment of AI capabilities in various applications, such as financial news analysis.