Abstract: Disclosed herein are systems and methods for graph-based dynamic information retrieval and synthesis, particularly for enhancing the response generation of chatbot applications using large language models (LLMs). The methods involve receiving a user query, generating a query embedding, and accessing a knowledge graph comprising nodes representing documents with associated document embeddings. A set of documents is retrieved based on similarity scores, and a distance matrix is constructed to calculate shortest-path distances between nodes. Documents are clustered to identify a largest cluster from which content is retrieved to formulate a response. The disclosed embodiments improve upon retrieval augmented generation (RAG) by leveraging the hierarchical relationships of a knowledge graph and node interconnectedness to provide contextually coherent information and mitigate the inclusion of information from irrelevant or superficially related documents in LLM responses.