Most modern AI systems rely on Retrieval-Augmented Generation (RAG) to ground large language models in private data. However, RAG alone cannot guarantee correctness when business rules, policies, or compliance constraints are involved.
RAG retrieves relevant documents from a vector database and injects them into an LLM prompt. This improves factual accuracy but remains probabilistic—the model may still hallucinate or misinterpret rules.
RETE is a rule-matching algorithm used in expert systems to efficiently evaluate large sets of IF–THEN rules. It provides deterministic, auditable decision-making—critical for enterprise workflows.
Together, they create AI systems that retrieve information, reason intelligently, and obey rules.
This architecture is ideal for HR, legal, finance, healthcare, and regulated industries.