Large language models (LLMs) suffer from several core shortcomings, stemming from the static nature of their knowledge and the shallowness of their reasoning, which together lead to hallucination problems. The main issues are:
1. Outdated knowledge — models cannot access new information generated after their training cutoff date. 2. Lack of deep reasoning — errors occur frequently when handling complex problems that require multi-step logical chains. 3. Hallucination — the generation of fabricated content that does not match facts. 4. Difficulty with complex tasks — such as integrating heterogeneous information from multiple sources and performing long-term planning.
To address these weaknesses, the structured knowledge augmentation paradigm — in particular, the evolution from RAG to RAS — brings a key breakthrough. It adds a knowledge structuring step between retrieval and generation, transforming unstructured text into organized, actionable knowledge graphs.
This shift delivers three core improvements:
1. Improved accuracy and timeliness of knowledge acquisition — by connecting to dynamically updated structured knowledge sources, the model always has access to the latest and most precise information. 2. More rigorous and explainable logical reasoning — leveraging the graph structure of knowledge graphs for multi-step inference makes the reasoning process clear and traceable, significantly reducing hallucinations. 3. Extended domain-specific expertise — by combining domain knowledge graphs, general-purpose models can be quickly transformed into "domain experts" with deep specialized knowledge.