Sepanta Zeighami

RAG Without the Lag: Enabling "What-If" Analysis for Retrieval-Augmented Generation Pipelines

Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with external knowledge. Given a user query, RAG pipelines retrieve (R) information from external sources, before invoking a Large Language Model (LLM), augmented (A) with this information, to generate (G) respo…