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A production-grade Self-Healing Retrieval-Augmented Generation (RAG) system built with FastAPI, LangChain, Qdrant, and modern AI engineering practices.
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
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
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
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
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
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 │
