Building a Private AI Document Search App: The Real Work Behind a RAG Stack

1. 1Build a RAG stack using vector databases and LLMs to enable semantic search
2. 2Configure the search infrastructure to index internal documents and data
3. 3Test the search app with sample queries to refine its accuracy
4. 4Apply fine-tuning to the LLM to improve search results
5. 5Compare the performance of different RAG architectures to optimize the search app

Developers and data scientists on a team can benefit from understanding the RAG stack to build efficient search infrastructure for internal knowledge

💡 A RAG stack is essential for building an efficient search infrastructure for internal knowledge

Build a private AI document search app using a RAG stack to unlock internal knowledge #RAG #AIsearch