Read/Write: Steps of Prompting

Chain of thought, tree of thoughts, and now graph of thoughts—a progression that may lead to agentic AI.

An abstract image of a flowchart

Key Points:

  • Researchers are exploring methods to enable large language models to display more human-like, emergent reasoning capabilities.
  • Chain of Thought and Tree of Thoughts approaches improved reasoning but were still fundamentally linear.
  • Human reasoning operates more like a graph, interconnecting thoughts in parallel, dynamic networks.
  • Graph of Thoughts (GoT) models allow language models to reason in an interconnected, non-linear graph structure.
  • In an initial test, GoT outperformed other techniques in a sorting task, increasing quality while reducing costs.
  • For complex planning, GoT could enable simultaneously considering multiple options and constraints.
  • This approach may allow more sophisticated reasoning and decision-making, closer to human cognition, though whether it enables emergent capabilities remains to be seen.

A recent paper on Graph of Thoughts reasoning highlights the progression towards Agentic AI, one of our Artificiality Pro Obsessions. As AI tools become increasingly autonomous and capable of handling complex tasks with minimal supervision, their reasoning abilities, task design, and capacity to recover from failures, is crucial.

To encourage Large Language Models (LLMs) to reason in more human-like ways, researchers have been exploring various methods, with a recent focus on enabling models to be more flexible and how they combine ideas. We see three big steps in prompt engineering techniques, each of which demonstrate a significant step up in the ability of prompt engineers to tap into the knowledge in a large language model: chain of thought, tree of thoughts, and recently graph of thoughts reasoning.

Initially, Chain of Thought reasoning was found to enhance the effectiveness of LLMs. By breaking down complex problems into simpler components, models could reason more effectively. Humans do this too: one of the best predictors of someone’s ability to solve a problem is how early in the process they break the problem up. Multiple chains of thought advanced this technique by enabling multiple independent paths to be generated but, even with this advance, reasoning is still limited because there is no way to perform any "local exploration" such as back tracking.

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