We wrote a guide to Retrieval-Augmented Generation (RAG) to help teams build AI assistants that are accurate, trustworthy, and grounded in real data. RAG enhances large language models (LLMs) by retrieving relevant information from external sources—such as documents, databases, or websites—and incorporating it into the model's responses. This approach mitigates the issue of AI hallucinations, where models generate plausible but incorrect information, by anchoring responses in verifiable data.

Implementing RAG requires more than just integrating an LLM; it necessitates a robust retrieval infrastructure. Key components include a well-structured knowledge base capable of storing vast amounts of information, support for vector similarity search to find semantically relevant content, and low-latency retrieval to ensure real-time responsiveness. Search engines like OpenSearch and Elasticsearch are well-suited for this purpose, offering scalable, flexible, and efficient retrieval capabilities.

Our guide delves into the architecture of RAG systems, the importance of a reliable knowledge base, and the role of search technologies in delivering accurate AI responses. By leveraging RAG, organizations can develop AI assistants that provide factual, contextually relevant answers, enhancing user trust and reliability. Dattell

For a comprehensive understanding, read the full article here: https://dattell.com/data-architecture-blog/retrieval-augmented-generation-grounding-ai-assistants-in-real-data-for-reliable-results/