In Part 2 of this series, we enhanced our AI agent with conversation memory, allowing it to remember previous interactions and maintain context across sessions. However, we discovered another critical limitation: when asked about company-specific information like travel policies, the agent couldn’t provide accurate answers.
Generic AI models are trained on broad internet data but don’t know your company’s specific policies, procedures, or domain knowledge. They might provide plausible-sounding but incorrect answers (hallucinations), which is unacceptable for business applications.
In this post, we’ll add domain-specific knowledge to our AI agent through RAG (Retrieval-Augmented Generation), allowing it to answer questions based on company documents with accuracy and confidence.
