RAG vs. LLM fine-tuning: Which is the best approach?

This post provides a comparative analysis of Retrieval-Augmented Generation (RAG) and LLM fine-tuning for enterprise AI use cases. It explains the mechanics, benefits, and ideal scenarios for each approach, emphasizing RAG's strength in providing real-time, contextual relevance and fine-tuning's advantage in domain fluency and task precision. The post also advocates for combining both methods, illustrating with an example of a legal AI assistant. It concludes by positioning Glean's Work Assistant as a platform that supports both RAG and fine-tuned models, enabling tailored AI solutions.