GraphRAG is overkill. This is what we do instead for knowledge graphs (www.minimumviablefounder.com)

A recent discussion highlights the limitations of the traditional approach to AI-driven knowledge management known as retrieval-augmented generation (RAG) and introduces a new solution for building effective "company brains" without incurring the high costs associated with knowledge graphs like GraphRAG. The core issue with RAG is its inadequate performance on complex and multi-step queries that hold real business value, prompting a shift towards a more adaptive system. The new method retains essential components from existing frameworks but simplifies the architecture to operate on standard databases rather than costly graph databases, thus making it self-serve for mid-market clients. The innovation involves a hybrid approach where data remains within conventional infrastructure, maintaining entity records that evolve based on usage without extensive preprocessing or custom schema design. Importantly, the method employs lazy summarization and adaptive entity resolution, ensuring that AI-generated insights scale with user curiosity rather than document volume. This new system effectively meets the demands of evolving business environments while minimizing operational overhead, proving to be efficient and cost-effective for organizations aspiring to derive actionable insights from their internal knowledge.