How Do Language Models Think?

Language models are AI systems that can understand, interpret, and generate human language. They have been trained on vast amounts of text data - everything from books and articles to websites and code - and they have access to the entire internet at any point in time. These models engage in conversations, answer questions, write content, and help solve problems through their understanding of language patterns.

At their core, language models process everything (your inputted data and its outputted data) as sequences of tokens - small pieces of text that could be words, parts of words, or even individual characters. When interacting with one of these models, there are a few key things that happen:

1. Input Processing - your text is broken down into tokens and the model analyses the relationship between these tokens. This step is what informs the conversation's context.
2. Pattern Recognition - the model considers your input, identifies relevant patterns (based from its training), and determines the most appropriate type of response to provide.
3. Response Generation - based on the conclusion drawn from training, the model generates new tokens one-by-one, based on what makes the most sense given the context.

This series of events demonstrates the importance of providing the four core components every single time you provide a response to the AI. Missing any part of these aspects could result in the model not producing an adequate response for your needs. The more specific and clear your prompt, the more tailored the response will be.

Let's look at a simple example. When asked "Write a story about a dog," a language model might generate: "Max was a playful golden retriever who loved chasing tennis balls in the park."

But if we add more context: "Write a story about a heroic police dog who saves lives," the model adapts its response based on this additional information, potentially generating: "Officer Max, a highly trained German Shepherd, used his keen sense of smell to locate survivors in the rubble after the earthquake."

Context is Key

Unlike humans who truly understand concepts, language models work by recognizing and reproducing patterns in text. Since they don't follow rigid rules, they rely on these patterns to facilitate the conversation and their ongoing training. Think of this experience like having a conversation with someone who can only remember the last few minutes of a conversation. Let's see an example:

You: "I have a cat named Luna."
AI: Acknowledges Luna
You: "She's black and white."
AI: Remembers Luna is a black and white cat
You: "What color is my pet?"
AI: Can tell you Luna is black and white

However, there are a variety of limitations, which can present challenges over longer conversations, especially if most of your prompts involve lengthier responses. If your chat goes on for too long and then you ask "what color is my pet?" the AI might not remember Luna or her colors. This is why it is crucial to maintain context throughout the conversation.

The Power and Limitations of Pattern Recognition

Language models are remarkably adept at recognizing patterns, but this capability comes with both advantages and constraints. When working with these models, it's essential to understand that they don't actually "know" anything in the way humans do. Instead, they process information based on statistical patterns they've learned during training.

For instance, a language model can expertly craft a professional email because it has analyzed millions of examples, understanding the common patterns in formal business communication. However, if you ask it for real-time information like "What's the current temperature in New York?" it can only discuss general patterns about New York weather – it cannot access current data unless specifically provided with it. This pattern-based approach affects how the model handles different types of tasks. When you initiate a conversation with AI, be sure to provide enough information that the model can be effective. Compare these approaches, which showcase the role that specificity and context play in pattern recognition:

Prompt 1 is likely to result in generic, unfocused information that may or may not fit your needs. However, prompt 2 provides more specific context that the model can use to provide practical value. Understanding how language models think directly impacts how we should write prompts. The key is to provide enough context and specific parameters to guide the model toward your desired outcome.

As we continue exploring prompt engineering, we'll delve deeper into how language models process text into tokens, and why this understanding is crucial for crafting effective prompts. This knowledge will help you optimize your interactions and achieve better results in your AI-assisted tasks.