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AI Prompt Engineering · Lesson

Tree-of-Thought Exploration

Branching and evaluating thoughts.

From Chains to Trees

Tree-of-Thought (ToT, Yao et al., 2023) generalizes chain-of-thought from a single linear path to a search tree of partial solutions. Each node is a coherent intermediate thought; branches explore alternative continuations.

This lets the model deliberate: generate multiple next-steps, evaluate them, keep the promising ones, and backtrack from dead ends, mimicking systematic problem solving rather than committing to the first idea.

class ThoughtNode:
    def __init__(self, state, parent=None):
        self.state = state      # partial solution / reasoning so far
        self.parent = parent
        self.children = []
        self.value = None       # evaluator score

The Four ToT Components

A ToT system has four design choices: thought decomposition (what is a step), the thought generator (how to propose next steps), the state evaluator (how to score partial solutions), and the search algorithm (BFS, DFS, or best-first).

Each is a separate prompt or policy. Designing ToT means specifying all four for your task.

tot = {
    'decompose': step_definition,   # e.g. one equation, one move
    'generate':  propose_thoughts,  # sampling or proposal prompt
    'evaluate':  score_state,        # value/vote prompt
    'search':    bfs_with_beam,      # BFS | DFS | best-first
}

All lessons in this course

  1. Chain-of-Thought Prompting
  2. Self-Consistency Sampling
  3. Tree-of-Thought Exploration
  4. When Reasoning Prompts Help
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