Leveraging Large Language Models for content analysis and generation for podcast transcriptions

This research project explores podcast transcription analysis and content generation, leveraging Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) framework. It examines a wide range of natural language processing (NLP) tasks, from summarization to question-answering, chapter segmentation, sentiment analysis, and more, in order to provide a comprehensive analysis of podcast content. The project aims to overcome the context window limitations of LLMs by introducing RAG. Ultimately, it seeks to enhance the accessibility, searchability, and engagement of podcast experiences, with potential applications beyond the podcast domain.