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In this demo, we will showcase how, using Gathr.ai’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.
- 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
