If you've built a Retrieval-Augmented Generation (RAG) system before, you've probably ended up with something like this: User Query │ ▼ Retrieve Documents │ ▼ Generate Answer │
One of the biggest limitations of most Retrieval-Augmented Generation (RAG) applications is that they assume the first retrieved answer is always good enough. In reality, retrieval isn't perfect. Some
When people build a Retrieval-Augmented Generation (RAG) system, the journey usually ends once the model produces an answer. For me, that's where the interesting part begins. After spending the previo
Week 2 of my AI Engineering journey focused on transforming SentinelRAG from a document indexing system into a complete Retrieval-Augmented Generation (RAG) pipeline. By the end of this week, the syst
In the previous article, I introduced SentinelRAG and explained why I wanted to build a production-oriented Retrieval-Augmented Generation system. This week was dedicated to building the foundation. I
Most Retrieval-Augmented Generation (RAG) tutorials end with retrieving a few documents and asking an LLM to answer a question. That's a great introduction, but it's far from how production AI systems
