Chain of Thought & Tree of Thought

Introduction to AI Chat Models’ Thinking

Learning How AI Thinks

Have you ever thought about how an AI Chat model comes up with its answers? Does it feel like us, or does it have a different method? In Module 5, we will investigate how conversational AI models work and think. We’ll focus on two fundamental ways these models approach thinking: the Chain of Thought and the Tree of Thought. By understanding these methods, you can leverage them to benefit your business.

The Chain of Thought is like a train ride. Each thought is a stop on the track, connected neatly to the next one, just like a train sticks to its channel. This way of thinking works best when you have a straight path from the problem to its solution.

On the other hand, Tree of Thought is more like a road trip with lots of junctions. Each junction is a thought or a decision, and you can go in many ways. Like conversational models, we have many thoughts to choose from, and each one can lead to new ideas.

Now that we’ve introduced these methods let’s explore how they can enhance your business.

Chain of Thought in AI Models

Researchers have discovered how to guide these programs to follow a chain of thought, mimicking the logical steps we take when solving puzzles.

The term “Chain of Thought” (CoT) also explains how the model generates text one piece at a time. Each word or part of a word is influenced by the words that came before it, forming a chain where each thought affects the next. The model uses the conversation context to decide the best response at each step based on patterns learned during training.

To illustrate this, imagine you have three apples and then get two more. To figure out how many you have now, you would think: “I had 3, then I added 2, so now I have 5.” This step-by-step process is what we call the Chain of Thought.

Researchers have found that teaching AI models using examples like this helps them get better at solving problems. What’s remarkable is that we can now follow the AI’s thinking process step by step, making it useful for many tasks, from doing math to answering everyday questions.

Let’s examine why the Chain of Thought is important in AI Chat models when solving issues and making decisions.

Tree of Thought in AI Models

In artificial intelligence, there’s a new concept called the “Tree of Thought.” Think of it as a decision-making process that AI Chat models use when they generate text.

Like the many branches sprouting from a tree’s trunk, each word or element of a sentence that the AI comes up with could lead to multiple possible next steps. The AI decides which ‘branch’ or path to follow next based on prior training and built-in algorithms.

To help you visualize this, consider a scenario where you’re navigating a labyrinth. You have multiple options at every turn and must choose which way to go. Similarly, while creating a sentence, the AI has numerous options after each word it produces, and it must select one based on its programming.

Let’s imagine we’re working with AI Chat models, and we share a simple story with them. The story goes like this: Bob is moving from one room to another and holding a ball inside a cup. We then ask the AI, “Where is the ball?” Ideally, it should understand from the story that the ball is in the cup that Bob is holding. However, some AI systems may need to connect the dots correctly and might give a wrong answer.

To help the AI understand better, we use a technique called “Chain-of-Thought prompting.” Think of CoT as a map of the AI’s thinking process. It’s like giving the AI a GPS that helps it understand the direction it’s taking and how it’s reaching its conclusions. But, like how we can sometimes still get lost even with a GPS, the AI might make mistakes even with this guide.

We introduced a “Tree-of-Thoughts” method to improve the AI’s accuracy. Picture a detective at a crime scene looking at clues from different angles before deciding who the culprit is. Similarly, the Tree-of-Thoughts method lets the AI examine a problem from various viewpoints and double-check its conclusions. However, this method usually needs several prompts, just like a detective may need to revisit the crime scene multiple times before solving the case.

In conclusion, these two strategies, Chain of Thought and Tree of Thought, represent different ways AI models solve problems and make decisions. They provide the AI with a map and compass, guiding it to reach accurate conclusions. By understanding these methods, you can better leverage AI Chat models for your business needs. Stay tuned for the next section, where we’ll explore the applications of each concept.