  The recent advancement of large language models (LLMs) represents a
transformational capability at the frontier of artificial intelligence (AI) and
machine learning (ML). However, LLMs are generalized models, trained on
extensive text corpus, and often struggle to provide context-specific
information, particularly in areas requiring specialized knowledge such as
wildfire details within the broader context of climate change. For
decision-makers and policymakers focused on wildfire resilience and adaptation,
it is crucial to obtain responses that are not only precise but also
domain-specific, rather than generic. To that end, we developed WildfireGPT, a
prototype LLM agent designed to transform user queries into actionable insights
on wildfire risks. We enrich WildfireGPT by providing additional context such
as climate projections and scientific literature to ensure its information is
current, relevant, and scientifically accurate. This enables WildfireGPT to be
an effective tool for delivering detailed, user-specific insights on wildfire
risks to support a diverse set of end users, including researchers, engineers,
urban planners, emergency managers, and infrastructure operators.
