Local LLMs

Knowledge Agents: Beat Frontier Models with Better Structure

James Wang introduces "knowledge agents" as a method to significantly enhance AI model performance for specialized and proprietary knowledge, often allowing smaller, locally-run models to match or exceed large frontier models. This is achieved by injecting highly structured, domain-specific information through embeddings, organized concept and thesis documents, and multi-pass search strategies. The approach effectively bridges modern LLMs with the precision of knowledge-based AI, solving challenges like hallucination and irrelevant information. This structure-first methodology provides expert-level answers for complex queries that even large frontier models struggle with alone.