Advanced RAG Architecture - Two comprehensive diagrams showcasing advanced Retrieval Augmented Generation (RAG) architecture. The first diagram illustrates the fundamental RAG pipeline from raw data extraction and preparation through embedding models, vector databases, to LLM-powered answer generation. The second diagram presents an advanced RAG system with sophisticated components including query improvement techniques (RAG Fusion, HyDE), intelligent query routing (logical and semantic), multi-database support (vector, graph, relational, text-to-cypher, text-to-SQL), retrieval optimization (RAG Fusion, CRAG), and prompt engineering for enhanced generation quality.
Two comprehensive diagrams showcasing advanced Retrieval Augmented Generation (RAG) architecture. The first diagram illustrates the fundamental RAG pipeline from raw data extraction and preparation through embedding models, vector databases, to LLM-powered answer generation. The second diagram presents an advanced RAG system with sophisticated components including query improvement techniques (RAG Fusion, HyDE), intelligent query routing (logical and semantic), multi-database support (vector, graph, relational, text-to-cypher, text-to-SQL), retrieval optimization (RAG Fusion, CRAG), and prompt engineering for enhanced generation quality.
Been orbiting around a few similar RAG setups recently — funny how the more “advanced” they get, the more the retrieval logic starts collapsing under its own abstraction layers.
Most of what looks like graph RAG or hybrid pipelines still ends up chunking without memory of what the meaning *was between chunks*. Like, once you break context down, it doesn't really come back unless you're actively managing the semantic deformation.
One approach we tried recently was treating the RAG sequence not as a pull→answer pipeline, but as a tension-mapping surface — almost like pressure zones of forgotten logic.
Not sure if this diagram leans more toward runtime structure or memory strategy, but either way, curious to hear how people are anchoring long-range meaning across hops.
Still feels like everyone's building ziplines across a canyon without checking if the cliffs are even stable.