diff --git a/pages/techniques/tot.en.mdx b/pages/techniques/tot.en.mdx
index 67e7392..4218ceb 100644
--- a/pages/techniques/tot.en.mdx
+++ b/pages/techniques/tot.en.mdx
@@ -31,3 +31,13 @@ Code available [here](https://github.com/princeton-nlp/tree-of-thought-llm) and
 
 At a high level, the main ideas of [Yao et el. (2023)](https://arxiv.org/abs/2305.10601) and [Long (2023)](https://arxiv.org/abs/2305.08291) are similar. Both enhance LLM's capability for complex problem solving through tree search via a multi-round conversation. One of the main difference is that [Yao et el. (2023)](https://arxiv.org/abs/2305.10601) leverages DFS/BFS/beam search, while the tree search strategy (i.e. when to backtrack and backtracking by how many levels, etc.) proposed in [Long (2023)](https://arxiv.org/abs/2305.08291) is driven by a "ToT Controller" trained through reinforcement learning. DFS/BFS/Beam search are generic solution search strategies with no adaptation to specific problems. In comparison, a ToT Controller trained through RL might be able learn from new data set or through self-play (AlphaGo vs brute force search), and hence the RL-based ToT system can continue to evolve and learn new knowledge even with a fixed LLM.
 
+[Hulbert (2023)](https://github.com/dave1010/tree-of-thought-prompting) has proposed Tree-of-Thought Prompting, which applies the main concept from ToT frameworks as a simple prompting technique, getting the LLM to evaluate intermediate thoughts in a single prompt. A sample ToT prompt is:
+
+```
+Imagine three different experts are answering this question.
+All experts will write down 1 step of their thinking,
+then share it with the group.
+Then all experts will go on to the next step, etc.
+If any expert realises they're wrong at any point then they leave.
+The question is...
+```