Retrieval Augmented Generation (RAG) is one of the most widely discussed use cases for LLMs. When you combine RAG with a reasoning LLM, the results become more reliable because you can see the thought process happening under the hood.
Why Document Quality Matters
For RAG to perform well, the documents you feed into it are crucial. “Garbage in, garbage out” applies not just to traditional ML models but also to LLM applications.
PDF is the most widely used document format, and it can be surprisingly difficult to extract clean information from it. Choosing the right parsing strategy is half the battle.
The Private Stack
The beauty of this approach is that everything runs locally. No data leaves your machine, making it suitable for sensitive enterprise documents.
Check out the full implementation walkthrough on YouTube.