Understanding How AI Works: A Guide for Non-techies

How AI Works

Large Language Models (LLMs) like OpenAI’s ChatGPT are becoming an increasingly important part of our daily lives. From chatting with virtual assistants to generating creative ideas, these AI systems power tools we use without much thought. But how do they actually work? And why do they seem so good at answering questions, writing essays, or even holding a conversation?

This article will break down the complexity of LLMs into simple, everyday language, using analogies to help you understand their magic.

What is a Large Language Model?

Think of an LLM as a very smart guesser. It’s a type of artificial intelligence trained on massive amounts of text from books, websites, articles, and more. Its goal is to predict what words or sentences are most likely to come next in a given context. For instance, if you say, “The sky is,” an LLM might predict “blue” as the next word because it has learned from countless examples where those words often appear together.

The key idea is that LLMs don’t “know” facts or remember specific books or articles. Instead, they’ve learned patterns, associations, and probabilities from the text they were trained on.

A Puzzle Piece Analogy: How LLMs Build Responses

Imagine you’re sitting in a room with a huge pile of puzzle pieces. But these pieces don’t belong to a single puzzle—they come from thousands of different puzzles you’ve seen before. Your task is to build a new puzzle that fits a specific theme someone gives you.

For example, if someone says, “Create a puzzle about a cat sitting on something,” you start looking through your pile of pieces. Based on what you’ve seen before, you know that certain pieces are more likely to fit together:

   •   “Mat” pieces are the most common and seem to fit 70% of the time.

   •   “Couch” pieces fit less often—20%.

   •   “Roof” pieces fit occasionally—5%.

   •   Other random pieces might work, but they’re rare—5%.

You pick the pieces that make the most sense (like “mat”) and assemble them into a complete response. This is similar to how LLMs generate text: they predict which words are most likely to come next based on patterns learned during training.

Probabilities, Not Certainties

A key part of this process is that LLMs work probabilistically, meaning they make educated guesses about what’s most likely to come next rather than retrieving a single “correct” answer.

For instance, let’s go back to the sentence, “The cat is sitting on the…”

The LLM doesn’t know what the next word should be. Instead, it calculates probabilities based on patterns in its training data:

   •   “mat”: 70% likely

   •   “couch”: 20% likely

   •   “roof”: 5% likely

   •   Other options: 5%

The model then chooses the most likely word (often “mat”) and keeps predicting the next word until the response is complete. This guessing game happens incredibly quickly, allowing the LLM to generate smooth, coherent sentences.

Personalization: Learning From Your Prompts

LLMs don’t just build one puzzle and stop. They can adjust their guesses based on what you’ve said before, temporarily “learning” from the context of your conversation.

Imagine you’re working on a series of puzzles with a friend. If they’ve asked you to build puzzles about cats and furniture several times in the past, you start to expect that theme. So when they next say, “The cat is sitting on the…,” you’re more likely to choose pieces that fit furniture-related ideas, like “couch” or “chair,” because you’ve picked up on their preferences.

Similarly, when you interact with an LLM, it uses the context of your previous inputs to make better guesses. For example, if you’ve been asking questions about home decorating, it might assume that “The cat is sitting on the…” is more likely to end with “couch” than “roof.”

However, this context is temporary. Once your conversation ends, the LLM doesn’t retain any memory of your past interactions.

Common Misconceptions About LLMs

1. Do LLMs Understand Language Like Humans?

No. LLMs don’t “understand” language the way people do. They don’t have thoughts, emotions, or intentions. Instead, they rely on patterns and probabilities to generate text that seemsthoughtful and intelligent.

2. Can LLMs Think Creatively?

Yes and no. LLMs can produce creative outputs by recombining ideas in novel ways. However, they’re not truly “creative” because they don’t have original ideas or experiences.

3. Do LLMs Remember What I Say?

Not permanently. LLMs can use the context of your current conversation to make better predictions, but they don’t retain that information once the interaction is over.

Everyday Applications of LLMs

LLMs are already part of your daily life, even if you don’t realize it. Here are some common ways they’re used:

   •   Customer Support: Chatbots powered by LLMs can answer questions, resolve issues, or guide you through processes.

   •   Writing Assistance: Tools like Grammarly or ChatGPT can help draft emails, essays, or reports.

   •   Search Engines: Modern search engines use LLMs to understand your queries and provide more relevant results.

   •   Personal Assistants: Virtual assistants like Siri and Alexa use LLMs to interpret commands and respond intelligently.

Limitations of LLMs

Despite their impressive abilities, LLMs have limitations:

1. Accuracy: LLMs can sometimes generate incorrect or nonsensical information because they rely on probabilities, not facts.

2. Bias: Since LLMs are trained on human-written text, they can reflect the biases present in that data.

3. Context Length: LLMs can only “remember” a limited amount of context, so they may lose track of long or complex conversations.

Bringing It All Together

Large Language Models might seem mysterious, but at their core, they’re just powerful tools for predicting what comes next in a sequence of words. By thinking of them as a puzzle-builder that uses patterns and probabilities, we can demystify how they work and appreciate their strengths and limitations.

Whether you’re asking an LLM to draft an email, answer a question, or help with creative writing, you’re leveraging the incredible ability of AI to make educated guesses and build coherent responses—one puzzle piece at a time.