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Articles tagged “RAG” on Ryshe.
Vector search is the default for RAG, but engineering and AEC documents are full of cross-references that pure vectors miss. Here is when graph-aware retrieval earns its complexity.
Most enterprise RAG implementations fail because teams treat retrieval as a search problem instead of a knowledge architecture problem. Here's how to build one that your organization will actually trust.
Your organization's most valuable knowledge lives in people's heads, scattered documents, and tribal processes. AI can change that — but only if you build the system around how people actually work.
Vector databases are the infrastructure layer behind every enterprise AI search and RAG system. Here's what they actually do, when you need one, and how to choose between the major options.
Semantic search understands meaning. Keyword search matches terms. Most enterprise systems need both. Here's a practical guide to choosing the right search architecture for your use case.
Choosing between Azure AI Search and Elasticsearch? This practical comparison covers cost, AI capabilities, vector search, managed vs. self-hosted trade-offs, and which one fits your architecture.