Build Hallucination-Free RAG with Verbatim

Verbatim RAG offers a novel approach to Retrieval-Augmented Generation (RAG) systems by focusing on text extraction rather than generation to eliminate hallucinations commonly introduced by Language Learning Models (LLMs). By constraining the model to extract exact text spans from source documents rather than generating new tokens, Verbatim RAG ensures that every part of the response is directly traceable to the original content, addressing the root cause of factual drift in traditional RAG systems. This method is easily integrated with existing systems like LangChain or LlamaIndex in a few lines of code and operates efficiently on a CPU-only pipeline, avoiding the costs associated with GPU or LLM API calls. Verbatim RAG supports various template management modes for response formatting and provides tools for metadata filtering, index inspection, and debugging. It is particularly suited for applications where precision is crucial, such as in medical, legal, or financial domains, while traditional RAG systems may still be preferable for tasks requiring synthesis across sources or natural language fluency.