Category: RAG Architecture

Enterprise RAG teams obsess over embedding models and reranking algorithms while overlooking the silent killer of retrieval accuracy: how documents are chunked. The irony is sharp—research shows that tuning chunk size, overlap, and hierarchical splitting can deliver 10-15% recall improvements that rival expensive model upgrades. Yet most teams default to uniform 500-token chunks without understanding…

The retrieval decision you make at the start of your RAG project quietly determines whether your system succeeds or fails at scale. It’s the choice between keyword-based search (BM25), semantic vector embeddings, or a hybrid approach that uses both. On the surface, this seems technical. In reality, it’s a strategic decision that impacts retrieval accuracy,…

The problem appears simple on the surface: your RAG system retrieves the right information, feeds it to your LLM, and gets back a response. But watch what happens in production. Your customer service bot retrieves perfect documentation but returns unstructured, contradictory information. Your compliance officer needs extracted data in a specific JSON format, but the…

Enterprise organizations generate knowledge across dozens of disconnected systems—Confluence wikis, Slack conversations, email threads, project documentation, customer support tickets, and legacy databases. When your AI assistant can only access one or two of these sources, you’re essentially building a digital librarian who’s only allowed to read picture books while ignoring the encyclopedias. This fragmentation creates…

The average enterprise loses 2.5 hours per day searching for information across scattered documents, databases, and knowledge repositories. Despite investing millions in enterprise search solutions, knowledge workers still struggle to find relevant, contextual answers when they need them most. Traditional search returns documents; what teams really need are synthesized insights that connect information across multiple…

The race to deploy real-time AI applications has exposed a critical bottleneck in traditional RAG architectures: latency. While organizations rush to implement retrieval-augmented generation systems, many discover their carefully crafted solutions crumble under production workloads, delivering responses that arrive seconds too late for real-time applications. Consider this scenario: A financial trading platform needs to provide…