  Retrieval augmented generation (RAG) provides the capability to constrain
generative model outputs, and mitigate the possibility of hallucination, by
providing relevant in-context text. The number of tokens a generative large
language model (LLM) can incorporate as context is finite, thus limiting the
volume of knowledge from which to generate an answer. We propose a two-layer
RAG framework for query-focused answer generation and evaluate a
proof-of-concept for this framework in the context of query-focused summary
generation from social media forums, focusing on emerging drug-related
information. The evaluations demonstrate the effectiveness of the two-layer
framework in resource constrained settings to enable researchers in obtaining
near real-time data from users.
