Tree-of-Thought & Graph Prompts
Discover advanced methods for exploring multiple reasoning paths and structuring complex problem-solving with LLMs using tree or graph-like structures.
Intro: Beyond Simple Chains
Welcome to advanced prompting! So far, we've explored techniques like Chain-of-Thought (CoT) which guide LLMs through step-by-step reasoning.
But what if a problem requires more than a linear path? What if there are multiple potential solutions or complex interdependencies?
Today, we'll dive into Tree-of-Thought (ToT) and Graph Prompts, powerful methods for tackling these challenges by allowing LLMs to explore diverse reasoning paths.
Recall: Chain-of-Thought
Before we branch out, let's quickly recall Chain-of-Thought (CoT) prompting.
- CoT encourages LLMs to show intermediate reasoning steps.
- It works great for problems solvable with a clear, sequential logic.
- The LLM generates one thought, then the next, in a linear fashion.
Think of CoT as following a single path through a maze until you find the exit.
All lessons in this course
- Chain-of-Thought Prompting
- Self-Consistency & Generated Knowledge
- Tree-of-Thought & Graph Prompts
- ReAct: Reasoning and Acting with Tools