This research introduces the Ibt Decision Framework, a structured control layer designed to improve the reliability, safety, and consistency of LLM-based systems. While Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) provide strong generative capabilities, they remain inherently probabilistic and lack execution control, often leading to unsafe or inconsistent behavior in real-world applications. The proposed framework separates generation from execution by introducing a multi-layered decision system that includes:- Intent detection- Validation mechanisms- Risk-aware execution- Structured workflow control (Ibtcode)- Failure handling and rollback support The system transforms LLM-based pipelines into controlled execution systems by ensuring that all actions are validated, risk-assessed, and safely executed. Experimental evaluation demonstrates improved stability and failure handling compared to baseline LLM + RAG systems, highlighting the importance of decision-driven architectures in production AI systems. This work bridges the gap between generative intelligence and deterministic system behavior, enabling safer and more reliable AI deployments.
ibtesham akhtar (Sat,) studied this question.