Introduction to Retrieval Augmented Generation (RAG)
Combining Information Retrieval and Generative AI for accurate and relevant responses
The AI Lab at the City of Amsterdam is exploring the potential of Retrieval-Augmented Generation (RAG), an innovative AI framework that combines the power Information Retrieval and Generative AI (e.g., LLMs) to generate accurate and contextually relevant responses.
This report introduces RAG, explaining its mechanism of combining user queries with relevant information from an external knowledge base to provide accurate and relevant answers. Unlike traditional LLMs, RAG incorporates a retriever component and a external knowledge base, resulting in improved response quality.
The report covers how RAG operates and the types of data it can handle, including text, images, audio, and videos. It also highlights the benefits of RAG, such as providing current information, increasing user trust, reducing AI hallucinations, and lowering training costs. The report discusses practical applications of RAG within the municipality, such as enhancing customer service chatbots, providing insights from documents, and offering personalized support to citizens.
The report also addresses the challenges and risks associated with RAG, including potential issues with database coverage, data accuracy, and the complexity of integrating diverse information sources. Ethical considerations such as bias, privacy, and environmental impact are also discussed.
This report aims to guide future initiatives in the development and responsible use of RAG within municipalities, maximizing the benefits of AI while mitigating potential risks.