Understanding Retrieval-Augmented Generation (RAG) in AI

AI-Powered Semantic Search: Craft Your Own RAG Model

Microsoft 365, Power Platform, AI, API Prompts, Semantic Search, Community Calls Retrieval-Augmented Generation (RAG) combines Information Retrieval and Generative AI. It is useful for semantic search, Q&A, and knowledge base augmentation. Core Components: Knowledge Base: Use sources like Azure Cognitive Search or databases. Document Indexing: Store documents in a vector database for semantic similarity searches. OpenAI Service: Utilize Azure…

Meet the Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases

Retrieval-augmented generation (RAG) enhances the output of Large Language Models (LLMs) using external knowledge bases. These systems work by retrieving relevant information linked to the input and including it in the model’s response, improving accuracy and relevance. However, the RAG system does raise problems concerning data security and privacy. Such knowledge bases will be prone to sensitive information that may be accessed viciously when …

Understanding Retrieval-Augmented Generation (RAG) in AI

Retrieval-Augmented Generation (RAG) is a transformative approach in artificial intelligence (AI) that enhances the performance of large language models (LLMs) by incorporating data from external, reliable sources. Unlike traditional LLMs, which rely solely on pre-existing training data, RAG connects these models to dynamic knowledge bases, enabling real-time, domain-specific responses. This innovation not only improves accuracy […] The post Und…