How RAG Architecture Works, Its Limitations, and ElevenLabs' Latency Optimization Case

RAG (Retrieval-Augmented Generation) is an architecture that helps LLMs reflect up-to-date information or private knowledge beyond their training data. By storing knowledge outside the model and retrieving only relevant passages for a query to add to the prompt, it can generate accurate responses without retraining.

How RAG Works and Its Process

RAG consists of three stages: Retrieval, Augmented, and Generation. Specifically, it follows a 5-step workflow, starting from the knowledge preparation stage of splitting documents into chunks, embedding them, and indexing them, through query processing, retrieving relevant passages, adding context, and generating responses. This allows bypassing the LLM's context window limitations and efficiently utilizing large knowledge bases.

Limitations and ElevenLabs' Optimization

RAG reduces the risk of hallucinations but does not eliminate them entirely. Key limitations include degraded retrieval quality, outdated source documents, inappropriate chunking, and added latency occurring during the retrieval process. In particular, response speed is a critical issue in real-time conversational applications.

ElevenLabs introduced a model racing architecture to address these latency issues. This approach sends queries in parallel to multiple rewriting models and adopts the first valid response. As a result, RAG's intermediate latency was reduced by half, from 326ms to 155ms, maintaining natural conversational flow even in large-scale knowledge base environments.