Reducing LLM hallucination by using a model-agnostic control layer [R]

About this article

We’ve been working on the hallucination problem from a systems perspective rather than a model perspective. Instead of trying to improve generation quality, we focused on constraining when a model is allowed to produce an answer. We ran a controlled benchmark: • 200 questions (100 answerable, 100 unanswerable) • Same base model across all conditions • Compared plain LLM, standard RAG, and our architecture • 3 independent AI judges from different model families Results: • Plain LLM: \~28% accu...