Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective

Retrieval-augmented generation (RAG) is a popular technique for Retrieval-augmented generation (RAG) is an effective way to use using large language models (LLMs) to build customer-support, large language models (LLMs) to answer questions while avoiding question-answering solutions. In this paper, we share our team's hallucinations and factual inaccuracy[12, 20, 46]. Basic RAG is simple: practical experience building and maintaining enterprise-scale RAG 1) search a knowledge base for relevant content; 2) compose a solutions that answer users' questions about our software based on prompt grounded in the retrieved content; and 3) prompt an LLM to product documentation. Our experience has not always matched generate output. For the retrieval step, one approach dominates the the most common patterns in the RAG literature. This paper focuses literature: 1) segment content text into chunks; 2) index vectorized on solution strategies that are modular and model-agnostic. For chunks for search in a vector database; and 3) when generating example, our experience over the past few years - using different answers, ground prompts in a subset of retrieved chunks[13].