  Large language models (LLMs) are capable of producing documents, and
retrieval augmented generation (RAG) has shown itself to be a powerful method
for improving accuracy without sacrificing fluency. However, not all
information can be retrieved from text. We propose an approach that uses the
analysis of structured data to generate fact sets that are used to guide
generation in much the same way that retrieved documents are used in RAG. This
analytics augmented generation (AAG) approach supports the ability to utilize
standard analytic techniques to generate facts that are then converted to text
and passed to an LLM. We present a neurosymbolic platform, Satyrn that
leverages AAG to produce accurate, fluent, and coherent reports grounded in
large scale databases. In our experiments, we find that Satyrn generates
reports in which over 86% accurate claims while maintaining high levels of
fluency and coherence, even when using smaller language models such as
Mistral-7B, as compared to GPT-4 Code Interpreter in which just 57% of claims
are accurate.
