  A common and fundamental limitation of Generative AI (GenAI) is its
propensity to hallucinate. While large language models (LLM) have taken the
world by storm, without eliminating or at least reducing hallucinations,
real-world GenAI systems may face challenges in user adoption. In the process
of deploying an enterprise application that produces workflows based on natural
language requirements, we devised a system leveraging Retrieval Augmented
Generation (RAG) to greatly improve the quality of the structured output that
represents such workflows. Thanks to our implementation of RAG, our proposed
system significantly reduces hallucinations in the output and improves the
generalization of our LLM in out-of-domain settings. In addition, we show that
using a small, well-trained retriever encoder can reduce the size of the
accompanying LLM, thereby making deployments of LLM-based systems less
resource-intensive.
