In my introduction to Chain of Thought (CoT) prompting, I demonstrated guiding a model to reason through a problem step by step rather than jumping straight to an answer. This popular technique often leads to more structured and accurate responses, especially for tasks involving logic or explanation.
With Tree of Thought (ToT) prompting, we extend that idea. Instead of guiding the model down a single reasoning path, we prompt it to generate text that presents multiple possibilities, simulates comparisons between them, and continues with the one that appears most promising in the text it generates, based on how the prompt is structured.
In this post, I’ll go over how ToT prompting works, how it differs from CoT, and when it makes sense to use it. As before, I’ll start with basic examples and then build up to more complex ones.
Author’s Note: “Tree of Thought” in this case refers to a structured prompting method, not a tool or formal framework. While the term has also been used in research involving more advanced tree search methods, this guide focuses on prompt-only techniques. It’s a concept that’s gaining recognition in the AI community and proving useful in practice—especially when the answer isn’t obvious from the start or when several approaches might lead to different outcomes.
How Tree of Thought Differs from Chain of Thought
The simplest way to understand the difference is that Chain of Thought (CoT) produces one line of reasoning, while Tree of Thought (ToT) works with several. In CoT, the model picks one approach and follows it. In ToT, the model is prompted to consider multiple options, explore their consequences, and compare outcomes.
This distinction becomes important when solving problems that can’t easily be broken down into a single sequence of steps. For example, a math problem with a clear formula may work fine with CoT. But if a task has many possible valid strategies (like a logic puzzle, a planning task, or a game situation) then committing to one path early may lead to failure. ToT lets us prompt the model to simulate several possible paths in the text, without actually tracking or remembering them. Each branch exists only as long as the prompt structure keeps it going.
In terms of structure:
- CoT = linear: A → B → C → D
- ToT = branching:
A → B1 → C1
A → B2 → C2
A → B3 → (abandon)
Evaluate C1 vs C2, choose better one.
You can think of it as turning a straight line into a branching process. That branching creates more opportunities to find a good answer, especially when there’s uncertainty, conflicting constraints, or multiple plausible starting points.
ToT usually involves more steps in the prompt. This might mean:
- Asking the model to list multiple possible first steps.
- Exploring each in turn.
- Comparing outcomes before settling on a final answer.
- Optionally, asking the model to reflect or revise.
Author’s Note: Because this is accomplished with prompting (just with a structure that encourages broader exploration before committing to a final output), this doesn’t require fine-tuning or special tools. However, more advanced versions of ToT, such as those using tree search algorithms, do involve external logic or tooling. Here, we’re focused on prompt-only methods.
A Basic Example: Revisiting the River Crossing Puzzle
To keep things consistent with the earlier post, we’ll return to the classic logic problem: a farmer needs to get a fox, a chicken, and a bag of grain across a river. The boat can only carry the farmer and one item. If left alone, the fox will eat the chicken, and the chicken will eat the grain.
A simple prompt like:
Solve the ‘Fox, Chicken, and Grain’ river crossing puzzle.
…will often result in a final answer, sometimes correct, sometimes not. There’s usually little explanation of why one step leads to another or what alternatives were considered.
With Chain of Thought prompting, we encouraged step-by-step reasoning:
Solve the ‘Fox, Chicken, and Grain’ puzzle. Think through each move the farmer can make. Consider what is safe or unsafe at each step and explain your reasoning.
This improves the result in many cases. But if the model makes a poor decision early on—taking the wrong item first, for example—it may commit to that path without recovering. It often won’t backtrack or try another approach.
Tree of Thought prompting addresses this by asking the model to generate and evaluate multiple possibilities. For example:
Solve the ‘Fox, Chicken, and Grain’ river crossing puzzle.
1. List all valid first moves.
2. For each one, describe the resulting situation and what options are available next.
3. If a path leads to an unsafe situation, indicate that it’s invalid and don’t continue describing it.
4. Continue describing only the valid paths until you reach a full solution.
5. Compare the successful paths in the generated text and summarize the most reasonable sequence of moves.
This structure guides the model to generate text that simulates different paths, filters out invalid ones, and continues with whichever path fits the prompt. The model isn’t making decisions or exploring alternatives. It is just continuing text based on the format we gave it. The steps are not complicated, but this structure often works better when simpler prompts go in the wrong direction early.
Example output comparison:
CoT: “Take the chicken first. Then return, take the fox…”
ToT: Option 2: Take grain — chicken is left with fox (unsafe). The text will now continue with Option 1 since Option 2 led to an unsafe situation.”
Use Tree of Thought prompting when your task involves uncertainty, multiple possible approaches, or when early mistakes can lead to failure.
Applying ToT to a Planning Task
Tree of Thought prompting becomes more useful as the number of reasonable options increases. Planning tasks are a good example, especially when multiple sequences could lead to acceptable outcomes and where constraints need to be considered along the way.
Suppose we ask the model:
Plan a 3-day itinerary for a first-time visitor to Rome.
A standard prompt will usually return a full itinerary right away. It may be fine. But often the model commits too early—stacking too many sites into one day, repeating activities, or skipping important details. There’s no reflection or comparison involved, just a single pass.
With a CoT-style prompt, we might say:
Plan a 3-day itinerary for a first-time visitor to Rome. Consider the structure of each day before filling in the details. Consider grouping activities by location and balancing historical sites, museums, and free time.
That adds some structure, but the model still tends to follow a single thread. If it chooses a poor structure, it typically won’t revise or compare against other options.
With ToT prompting, we ask the model to generate a few possible approaches before committing to one:
Plan a 3-day itinerary for a first-time visitor to Rome.
1. List two or three different ways to structure the trip (by geography, by theme, by time-of-day).
2. For each structure, outline a rough itinerary.
3. Identify tradeoffs—coverage, efficiency, variety.
4. Identify the structure that seems most effective in context and expand it into a complete plan.
This prompt leads the model to present multiple strategies before selecting one to expand on. It creates room to compare options and avoid premature decisions, which is especially useful when the task has many reasonable answers.
Example output comparison:
CoT: “Day 1: Colosseum, Vatican, Pantheon… Day 2: Trevi Fountain, Spanish Steps…”
ToT: “Approach A: Organize by region. Approach B: Alternate indoor and outdoor. Approach C: Prioritize top attractions. Evaluate: A is most efficient, B has variety. Choose A and expand.”
Using ToT for Multi-Step Math Problems
Math word problems often have a clear solution path, but not always. Some require choosing between different approaches—algebra, arithmetic, or proportional reasoning—and misreading the structure early can lead the model down the wrong track.
Here’s a simple example:
A train leaves Station A heading toward Station B at 60 km/h. Another train leaves Station B heading toward Station A at 40 km/h. The stations are 200 km apart. When do the trains meet?
A straightforward prompt often gives the right answer without much explanation. CoT improves this by prompting the model to go step-by-step:
Solve the train problem step by step. Start by defining known values, set up an equation, and solve for the time when the two trains meet.
This works most of the time, but when the numbers are harder or the wording is ambiguous, the model may choose an incorrect approach and follow it all the way to the end without reconsidering.
ToT prompting helps mitigate this by structuring the prompt to generate multiple possible approaches first:
Solve the train problem.
1. List a few different ways the problem could be solved (e.g., using relative speed, individual distance equations).
2. For each method, outline how it would work and what values need to be found.
3. Identify which method seems simplest or least error-prone.
4. Solve using that method.
5. If possible, briefly verify the result using one of the other methods.
This format sets up the model to simulate multiple alternatives and pick a more robust path before moving into calculation. If the first idea is flawed or harder to follow, it can shift to another.
Example output comparison:
CoT: “60 + 40 = 100 km/h. 200 ÷ 100 = 2 hours. Answer: 2 hours.”
ToT: “Approach 1: Relative speed (100 km/h) → 2 hours. Approach 2: Time = D / (V₁ + V₂). Approach 3: Set up equations for each train. Relative speed is simplest. Use that: 2 hours. Cross-check with equations: consistent.”
Practical Example: Choosing a Project Approach
Tree of Thought prompting is especially helpful when there are several valid options and no obvious best choice. Here’s a simple planning scenario:
Your team has 6 weeks to build a working prototype. You’re choosing between:
1. Using familiar tools to move quickly.
2. Trying a newer, faster framework that requires learning.
3. Outsourcing one part of the work.
Decide which approach to take.
A standard prompt might choose one option and explain why. A Chain of Thought (CoT) prompt adds reasoning, but still often commits too early. Tree of Thought (ToT) prompting encourages a broader comparison:
For each option, list pros and cons across time, effort, and risk.
Then compare them and pick the most balanced solution.
Optionally, consider combining ideas.
This guides the model to simulate different strategies, weigh tradeoffs, and possibly blend ideas. That flexibility often leads to better, more practical results.
Example output comparison:
CoT: “Option 1 is safest. We’ll go with it.”
ToT: “Option 1: Fast and familiar. Option 2: Potentially faster, but risky. Option 3: Saves time, but less control. Suggest combining 1 and 3 to balance speed and reliability.”
Bonus: How Models Simulate Reason
Language models don’t actually reason or compare ideas the way people do. They generate one token at a time based on patterns learned during training, not through logic, memory, or deliberate thinking.
When a prompt asks the model to list options, compare outcomes, or eliminate bad ideas, it’s not making decisions. It’s continuing text that resembles decision-making because the prompt is structured that way. The appearance of reasoning comes from the way we shape the input, not from internal understanding or problem-solving.
Tree of Thought prompting works by shaping the prompt so the model generates multiple paths and a choice between them. The model isn’t exploring or deciding anything. It’s just continuing text in the pattern the prompt sets up.
The clearer your structure, the more useful the output tends to be. But it’s still just pattern completion, not actual exploration or thought.
Using Regeneration to Simulate Branching
Tools like ChatGPT make it easy to get different perspectives by regenerating the response. Each new generation can simulate a different approach to solving the problem (not because the model is exploring, but because its outputs vary based on randomness and the way the prompt is interpreted each time).
This can be extended by copying these different responses into a new prompt and asking the model to compare them:
Here are three different responses to the same prompt. Compare them. Which one addresses the problem best and why?
This approach achieves a similar result to ToT prompting, especially when the model doesn’t naturally branch within a single response. Just remember: comparisons are still based on language patterns, not deep evaluation or critical judgment. But structured prompts can push the model closer to useful output.
Conclusion
Tree of Thought prompting builds on the same principle as Chain of Thought: Helping the model produce more structured and thoughtful output. But instead of a single line of reasoning, it prompts the model to explore multiple alternatives, compare them, and choose between them.
This tends to work better for problems with ambiguity, multiple strategies, or where early missteps can lead to failure. You’re not changing what the model is doing internally, but rather shaping the output format in a way that can lead to better answers, especially in complex tasks.
ToT prompting is easy to try. You can implement ToT in two ways:
- As a single structured prompt that asks the model to present multiple approaches within one response.
- By running a prompt multiple times, collecting the different responses, and then asking the model to compare these collected responses in a new prompt. Like CoT, its usefulness depends on the task, the clarity of your prompt, and the capabilities of the model.
It’s not applicable to every problem and it doesn’t guarantee better results, but when facing a complex task with multiple viable paths or no obvious first move, ToT prompting is a practical tool worth reaching for.
AightBits 
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