An automated helpdesk chatbot can be created using a combination of text-rich data sources, data integration solutions, and scripting languages. This process is exemplified by leveraging Zendesk data hosted in an S3 data lake, using Python, OpenAI’s GPT-4, ChromaDB for a vector database, and the LangChain API. The workflow begins with setting up a Fivetran connector to sync Zendesk data to a data lake or warehouse. The next steps involve extracting, transforming, and vectorizing data to load it into the vector database, followed by setting up a user interface for prompt acceptance. The retrieval model is built by vectorizing and storing data in a database, allowing for RAG operations that search the database for relevant information and augment user prompts before processing with the LLM. Streamlit is used to create a front end for user interaction, and the workflow is designed for easy updates and comprehensive responses as the vector database is enriched. The potential for further innovation includes streamlining the process, integrating functionalities, and using knowledge graphs, highlighting the evolving landscape of generative AI and data integration technologies.