Tree of Thought (ToT) prompting is an advanced framework for language model inference, developed to improve upon the limitations of the Chain of Thought (CoT) technique, by introducing a strategic, multi-path reasoning approach. Proposed by researchers including Yao et al. in 2023, ToT employs advanced search algorithms like breadth-first search, depth-first search, and beam search to navigate complex problem spaces, thereby enabling language models to engage in trial and error, backtrack, and self-evaluate as they work through problems. This technique is designed to enhance large language models' capabilities in solving intricate tasks such as puzzle games, creative writing, and decision-making problems by allowing them to explore multiple reasoning paths simultaneously. The effectiveness of ToT prompting is demonstrated by its higher accuracy in benchmark tests compared to CoT, making it a valuable tool for developers looking to build applications that require sophisticated reasoning and strategic planning. Helicone facilitates the optimization and evaluation of ToT prompts, offering a platform for experimenting with and refining these prompts for better performance.