  Implementing Retrieval-Augmented Generation (RAG) systems is inherently
complex, requiring deep understanding of data, use cases, and intricate design
decisions. Additionally, evaluating these systems presents significant
challenges, necessitating assessment of both retrieval accuracy and generative
quality through a multi-faceted approach. We introduce RAG Foundry, an
open-source framework for augmenting large language models for RAG use cases.
RAG Foundry integrates data creation, training, inference and evaluation into a
single workflow, facilitating the creation of data-augmented datasets for
training and evaluating large language models in RAG settings. This integration
enables rapid prototyping and experimentation with various RAG techniques,
allowing users to easily generate datasets and train RAG models using internal
or specialized knowledge sources. We demonstrate the framework effectiveness by
augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG
configurations, showcasing consistent improvements across three
knowledge-intensive datasets. Code is released as open-source in
https://github.com/IntelLabs/RAGFoundry.
