Conversational Geographic Question Answering for Route Optimization: An LLM and Continuous Retrieval-Augmented Generation Approach

We present a pilot study exploring the potential of Large Language Models (LLMs) to interface with application programming interfaces through logical instructions, specifically within the domain of Geographic Question Answering for route optimization. This study employs a Continuous Retrieval-Augmented Generation approach combined with fine-tuned LLMs, featuring customized node-based storage and vector search retrieval. We also provide a comparative analysis of the method’s effectiveness and adaptability in handling diverse textual queries.