I recently completed a project as part of my AI Engineering learning journey: an Agentic RAG AI Agent that answers user questions based on a provided knowledge base.
The main goal of this project was to build an AI assistant that does not simply rely on its general knowledge. Instead, it first searches a knowledge base, retrieves relevant information, and then generates an answer using that retrieved context.
What is the idea behind the project?
Large Language Models can sometimes answer questions using their pre-trained knowledge, even when that information is not available in the documents provided to the system. To address this, I built a Retrieval-Augmented Generation (RAG) pipeline.
The workflow looks like this:
