Enabling Gen AI-powered conversational experiences on enterprise content

In this demo, we will showcase how, using Gathr’s GenAI fabric, you can build a GenAI-powered chatbot to enable your employees to interact with your enterprise knowledge base, get answers to their questions, and in-turn improve their productivity and business impact.

We’ll build the chatbot using the RAG framework, which integrates domain-specific knowledge and addresses challenges like hallucination in large language models (LLMs).

First, we’ll build a data engineering pipeline to fetch data from various enterprise sources, parse it, generate embeddings, and then ingest it into the vector database. Post that, we will develop a frontend application that allows employees to get relevant answers, instantly, by augmenting the LLM with the vector database.

Key demo takeaways:

• Build RAG based chatbot for production
• Collect and prepare the data for embeddings
• Populate vector database, integrate it with LLM
• Build the chatbot application for employees
• Monitor and improve the chat prompts