Probably aims to prevent hallucinations and factual errors in LLMs by building a rigorous error-catch system, utilizing a deterministic validator to refine context and reduce ambiguity. This approach enables smaller AI models, reducing costs and token usage, and can be applied to various precision-sensitive use cases.