Conversational Support Summarization with Large Language Models

In this project, I spearheaded the development of a solution aimed at automating the summarization of customer service support conversations. The primary objective was to provide management with succinct insights into the interactions transpiring through our customer service chat platform.

My approach was anchored in harnessing the power of state-of-the-art Large Language Models (LLMs) coupled with a meticulously designed data pipeline architecture. By fine-tuning and instructing the LLM model, specifically leveraging the FLAN-T5 model from Huggingface, I ensured it was adept at distilling the essence of conversations effectively.

Throughout the project lifecycle, I navigated through multiple phases, beginning with initial tuning and instruction of the LLM model using openly available datasets. As we progressed, the model underwent iterative refinements, fueled by the accumulation of hand-crafted summaries derived from real support conversations.

To bring this solution to fruition, I orchestrated its encapsulation and seamless deployment into our production environment using the robust Dagster data pipeline framework. This framework orchestrated the flow of data, from extracting full conversations from our customer service chat system, to invoking the LLM-based summary generation, and finally persisting the distilled insights into a Mongo database.

The culmination of this endeavor was a sophisticated system capable of generating summaries of customer support conversations, empowering management with actionable insights gleaned from the wealth of interactions occurring within our customer service ecosystem. This project not only showcased my technical prowess but also underscored my ability to architect innovative solutions that drive efficiency and productivity within organizational workflows.