This paper presents novel contributions to improving large language models (LLMs) through the integration and enhancement of three reasoning frameworks: Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), and Retrieval-Augmented Thoughts (RAT). CoT generates linear intermediate reasoning steps, ToT introduces a branching structure for exploring multiple reasoning pathways, and RAT incorporates real-time external knowledge retrieval. A key innovation discussed is the use of decision trees, both handcrafted and automatically generated, to guide and validate the reasoning processes of CoT and ToT. This approach enhances the interpretability, accuracy, and adaptability of LLMs, particularly in complex problem-solving tasks like medical diagnostics and financial forecasting.