RAG

Chunking, vector search, and guardrails — how we build retrieval-augmented generation systems that stay accurate.

Hierarchical RAG: Building Multi‑Level Retrieval for Scalable Knowledge Systems

Introduction A language model is only as good as what it can retrieve. As knowledge bases grow into millions of documents, standard Retrieval-Augmented Generation starts to crack, not because the models are weak, but because the retrieval layer is too shallow. A single-level vector search often pulls back chunks that

AI RAG

Why Smarter Chunking Matters More Than Bigger LLMs

Introduction In specialized fields, the goal is a smarter system, not just a bigger one. Scaling large language models gets a lot of attention, but real gains often come from fixing the backend, and in a Retrieval-Augmented Generation pipeline, that backend is largely chunking. At Hoomanely, we kept running into