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README.md

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The Tree-of-Thought framework bears resemblance to an **organizational decision-making process** involving multiple stakeholders. The structure tries to emulate consensus and diversity of thought.
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The effect can be extended by using multiple agents that are fine tuned to handle different parts of a task. AutoGPT, where it self-evaluates LLM plans against a goal. Using differently tuned LLMs for specialisation or even completely different LLMs would allow greater diversity of thought. These LLMs could interact in different modes (using Team Topologies terminology):
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The effect can be extended by using multiple agents that are fine tuned to handle different parts of a task. AutoGPT is a good example of this, where it self-evaluates LLM plans against a goal. Using differently tuned LLMs for specialisation or even completely different LLMs would allow greater diversity of thought. These LLMs could interact in different modes (using Team Topologies terminology):
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* **Collaboration**: This could involve combining and iterating the outputs of multiple models to generate a final response to a user's input. For example, a general-purpose model might generate a preliminary response, which is then refined or enhanced by a specialized model.
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* **X-as-a-Service**: Some models might provide services to others, such as pre-processing input data, post-processing output data, or providing contextual information. These models would operate in a service role, supporting the functions of the other models.

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