How LLMs Like ChatGPT Work
When we talk about large language models like ChatGPT, it’s easy to get lost in the complexity of how these models function. But let's break it down into a few digestible steps—starting with the basics. There are several major large language models (LLMs) developed by companies like Google (PaLM), Meta (LLaMA), Anthropic (Claude), and others, each bringing their unique techniques to natural language processing. Despite differences in training data, architectures, or optimizations, all LLMs share a core principle: they use transformer architectures to generate coherent and contextually relevant text based on patterns learned during training. While these models are advancing the field of AI in their own ways, this article will primarily focus on OpenAI's newer iterations like GPT-4o, GPT-4o-mini, GPT-o1-preview, and GPT-o1-mini, as they represent key milestones in OpenAI’s ongoing development of conversational AI.
1. Training Phase
Before ChatGPT—or its newer versions—can respond to any question, it undergoes an intense training phase. The model is fed an immense dataset full of human language, including books, websites, and various forms of media. Imagine teaching a student not just grammar but also nuance, tone, and context. It's similar to how these models work—only instead of teaching them once, their "learning" is supercharged by reading through countless examples.
Now, here's something crucial to understand: AI models like GPT-4o, GPT-4o-mini, and others do not learn or evolve in real-time. They don't have the ability to grow more intelligent or autonomous as you interact with them. All their "knowledge" comes from the training phase, which is static. They generate responses based on the data they were trained on, and once deployed, they don’t have any mechanism to learn or adapt by themselves.
So, when people worry about AI becoming sentient or taking over the world, it’s important to emphasize that current AI, as we know it, has no consciousness, self-awareness, or decision-making capacity. It doesn’t possess any will or desire. It’s essentially a very sophisticated pattern-recognition system. The fears of AI suddenly becoming sentient and deciding to harm humanity are more aligned with science fiction than with the reality of how AI functions today.
In fact, even the most advanced models, like GPT-o1-preview, are entirely driven by the data they've been trained on and the parameters set by their developers. They follow instructions, generate text, and that’s it. There’s no hidden intelligence plotting behind the scenes. The real focus of AI development is on improving usability, fairness, and efficiency—making tools that assist us, not ones that decide for themselves.
By understanding this, we can better appreciate AI for what it is: a tool designed to serve us, not replace or dominate us. The fears of AI "taking over" reflect anxieties about the unknown, but with clearer insights into how AI actually works, it's easier to see how far we still are from anything resembling sentience or world domination.
2. Learning Structure
One of the things that makes these AI models so powerful is their structure. They’re based on something called a transformer architecture. It's essentially a method that lets the model "pay attention" to different parts of a sentence and focus on the most relevant bits when predicting what comes next.
Here’s where 4o and its mini counterparts take things up a notch: They’ve been fine-tuned for specific tasks. For example, GPT-4o-mini is designed to handle shorter, more concise responses, making it particularly useful for quick interactions or real-time applications where speed matters. Meanwhile, o1-preview models bring experimental features and improvements, including more efficient processing of larger contexts, allowing for more detailed and coherent responses over extended conversations. This structure makes all the difference in how well these models "understand" and generate coherent responses, even when dealing with complex queries.
3. Processing Your Prompt
When you ask ChatGPT to explain a concept or analyze a Bible passage, the system generates a response by pulling from patterns it learned during training. It doesn’t “know” anything the way we do. But if it has seen similar language in its training data, it can piece together a surprisingly coherent and contextually appropriate answer.
Here’s a cool, lesser-known insight: The newer o1-preview model is designed to handle subtle shifts in tone and context even better than its predecessors. It can navigate conversational shifts without breaking a sweat, which wasn’t always the case in earlier versions. 4o models can also "infer" nuances in user input better, making their responses feel more customized to the interaction.
4. Role of Neural Networks
So how does all this magic happen? It’s neural networks. ChatGPT and its successors break your input down into tokens—small chunks of text like words or phrases. The neural network then processes these tokens in relation to one another, calculating probabilities for what comes next. It’s like a really fast puzzle-solving machine, figuring out how to complete sentences based on all the patterns it has seen before.
With the o1-preview models, there's a bit more sophistication under the hood. These newer iterations have been designed to manage larger, more complex tokens efficiently, which means they can handle longer conversations or more intricate tasks without losing the thread. Plus, the model is more adept at multi-step reasoning, which comes in handy for problem-solving tasks or detailed explanations.
5. Generating Responses
Generating a response isn’t just a matter of picking the next word. Models like 4o and its mini versions use something called beam search to evaluate multiple possible next words or phrases. The system then selects the most likely sequence, leading to a coherent response. This method ensures that the model’s output is both accurate and fluid.
Here’s a fun fact: The "mini" models, like 4o-mini, are optimized for faster responses by narrowing the beam search parameters. This makes them particularly well-suited for applications where response speed is critical, such as chatbots or customer support systems, without sacrificing too much accuracy.
6. Speed and Efficiency
The sheer speed at which these models generate responses comes down to a blend of advanced hardware, like GPUs, and the efficiency of the transformer architecture. It’s all about computing probabilities fast enough to feel like a real-time conversation. But that’s not all. o1-preview has been developed with even more computational optimizations, allowing it to deliver high-quality responses quicker and more resource-efficiently than older models.
What sets models like 4o and o1-preview apart from GPT-4 is their targeted improvements. Whether it’s handling nuanced conversations or offering faster response times in a business setting, each new iteration brings something fresh to the table, pushing the boundaries of what these AI systems can do.
Does the Neural Network Learn As People Use It?
One common misconception about AI, especially tools like ChatGPT and its advanced versions like 4o, is that they learn from individual interactions in real time. Let's clear that up: they don’t. The learning and improvement of a neural network happen primarily during its training phase, not when you're using it day-to-day.
1. Initial Training
During the initial training phase, the model is fed a massive amount of data—books, articles, and websites. This is where the heavy lifting happens. The model learns to predict language patterns, how sentences are constructed, and which words are likely to follow others. All the "learning" that makes ChatGPT, 4o, or o1-preview effective is baked in at this stage. Once deployed, the model isn't actively learning or changing as people interact with it.
2. Post-Deployment Learning
So, what happens after the model is deployed? The short answer is: it doesn’t learn in real time. For instance, imagine you're chatting with 4o and you repeatedly ask it for the capital of France. Even if you told it Paris was incorrect (which it isn’t, of course), the model wouldn’t adapt to that feedback in your session. It would still confidently tell the next person that Paris is the capital of France because it's based on patterns it learned during training.
For science, I did alittle experiment where I kept telling ChatGPT that 2 + 2 equals 5, just to see if it would "learn" from that input. Despite multiple corrections, the model continued to say 2 + 2 equals 4—because it simply doesn’t learn in the same way humans do. This shows that while the model can "mimic" understanding, it’s just responding to patterns, not absorbing new information.
3. Model Updates
But what if there’s something wrong with the model’s knowledge? Developers can look at usage patterns and common errors users encounter, then incorporate that feedback into the next version of the model. For example, let’s say 4o often struggles with financial jargon. OpenAI could gather insights from interactions and retrain the model to handle these topics better in future iterations. This doesn’t mean the model is learning on the fly, but it does highlight how feedback loops exist between user interactions and future updates.
4. Separate Training and Inference
It’s important to understand the distinction between training (where the model learns from vast amounts of data) and inference (when the model generates responses to your prompts). During training, the model is essentially developing its ability to predict and generate language. During inference—which is what happens when you interact with it—the model uses what it learned but doesn’t learn from each interaction.
Think of it this way: It’s like a chess player who’s studied thousands of games before sitting down to play. During a match, they rely on their training, patterns they’ve memorized, and strategies they’ve practiced, but they’re not learning new strategies during the match itself. They’re simply drawing from what they already know. The same goes for GPT models during inference.
5. Resource Intensity of Training
One reason why models don’t learn in real time is because training large neural networks is extremely resource-intensive. Imagine trying to retrain a model like 4o, which is already dealing with billions of parameters, after every single conversation. It would take an astronomical amount of computational power. This is why learning happens in big updates, not in every single interaction.
Asking ChatGPT to "learn from every conversation" would be like trying to teach a library every time you checked out a book. The library holds a wealth of knowledge, but it’s not changing just because you’re borrowing a few books. The bookshelves—and in this case, the neural network—stay the same until a new batch of updates comes along.
What is the Role of System Prompts to Start Off a Chat?
When you start a conversation with a language model like 4o, o1-preview, or any of its mini variants, there’s something working in the background that you may not even realize: system prompts. These prompts set the tone, context, or even persona for the model, shaping how it responds to your questions or instructions.
1. Setting the Context
A system prompt is like an invisible instruction given to the model, telling it how to behave. You might provide your own prompt, such as, "Act as a world-class scientist" or "Respond like a helpful assistant." But even if you don’t give an explicit role, the model is often guided by a predefined system prompt, set by the developers, to act in a neutral, helpful, and conversational way.
Here’s an interesting nugget: The system prompts behind newer models like o1-preview have become increasingly sophisticated. OpenAI has experimented with prompts that help the model maintain specific tones or levels of expertise depending on the task at hand. This is why you can ask the same question across different contexts (say, in a casual conversation vs. a more formal scenario) and receive answers that feel tailored to the situation.
2. Influence on Responses
Once a system prompt is set, it frames the model's responses. If you’ve ever asked ChatGPT to “act as a lawyer,” you’ll notice that the responses suddenly take on more formal language, referencing legal principles or hypothetical cases. The model isn’t becoming an expert lawyer—what it’s really doing is matching patterns it has seen during training that correspond with legal language and tone.
I tried an experiment where I asked the model to act as a pirate for a few exchanges. The transformation was instant. “Arrr, matey!” and “Shiver me timbers!” peppered the conversation, but the underlying content was still coherent. The system prompt had only affected the style, not the factual accuracy of the response.
3. Limitations of the Model
Of course, it’s important to remember that while these prompts can guide the model’s tone and content, they don’t give the model actual expertise or understanding. So, when you ask 4o to “act as a medical professional,” the model isn’t suddenly a doctor. It’s just drawing from patterns in medical texts it has been trained on. That’s why you’ll often see a disclaimer reminding you that it’s not a substitute for professional advice.
For instance, even though o1-preview may be designed to handle specialized roles better, it still relies on patterns in the data, not personal experience or real-time updates. If you ask it to explain a complex medical condition, it will generate a response based on what it learned from texts, but it won’t provide insights beyond what was part of its training.
4. Consistency Across Interactions
Once the system prompt sets the context, the model tries to maintain that tone throughout the conversation. If you tell it to respond like a financial advisor, it will stick to that role until you either introduce a new system prompt or finish the session. This makes it easier for the model to stay consistent, whether you’re asking about stock markets or personal finance tips.
I used [System Prompt Master][5] to create a system prompt for a personal fitness coach. What was fascinating was how the model stuck to the coach persona, giving motivational feedback like “You’ve got this!” and “Consistency is key!” for several messages. It was like chatting with an upbeat trainer—but again, it was all based on patterns from fitness texts, not actual coaching experience.
For more info on custom GPTs, check out [5]: https://chatgpt.com/g/g-eb17yub89-system-prompt-master
5. Adapting to New Prompts
What’s even more impressive is how flexible the model can be when you provide new system prompts mid-conversation. You could be halfway through a chat about history, then switch to “Act as a philosopher,” and the model will smoothly transition into that role. This flexibility makes the newer models like 4o and o1-mini particularly powerful for handling diverse tasks without breaking the conversational flow.
For example, let’s say you ask the model to debate the ethics of artificial intelligence and then pivot to something lighter, like a travel recommendation. The model will shift its tone to match each topic, adapting to the new prompt without needing to "reset" or lose track of what you discussed earlier.
In short, system prompts are a crucial part of shaping your interactions with models like 4o and o1-preview. They set the tone, guide the response style, and help maintain consistency throughout the conversation. While the model doesn’t gain actual expertise through these prompts, it can still generate relevant, role-based responses by leveraging patterns it’s seen during training.
System prompts are like the hidden stage directions in a play: they tell the model what role to perform, but the model is still operating within the boundaries of its pre-learned knowledge. It’s a clever way to make AI feel more interactive and adaptable, even though it's just a very advanced form of statistical pattern recognition.
Context Windows
One of the impressive features of ChatGPT, and especially its newer versions like 4o and o1-preview, is its ability to hold a conversation that feels coherent over time. But there’s a limit to how much context the model can remember during a single session, and that’s where context windows come into play.
1. Context Window
The context window refers to how much of the conversation the model can "remember" at any given moment. Essentially, the model can only retain a certain number of tokens (words or parts of words) in its short-term memory, so to speak. This window is finite, typically capped at a few thousand tokens, depending on the version of the model.
With 4o, for instance, the context window might be around 8,000 tokens, while the o1-preview models boast even larger windows, sometimes up to 32,000 tokens. These larger windows are particularly useful when working on complex tasks that require the model to retain a lot of information over an extended conversation. However, even these newer models will eventually hit their limit and start to "forget" earlier parts of the discussion.
2. Limit of Back-and-Forths
The ability to handle a long conversation depends heavily on the length of each exchange. If you’re sending short, concise messages, the model can manage a greater number of back-and-forths before reaching the context limit. But if your exchanges are lengthy, it fills up that memory faster.
For example, let’s say you’re discussing the nuances of climate change policy with o1-preview. As the conversation gets longer, the model will eventually start trimming out older parts of the discussion to make room for new information. That’s why, if you reference something from much earlier in the conversation, it might not "remember" the exact details anymore.
For science, I had lengthy brainstorming session with 4o for writing a script. I found that after about 15 exchanges, the model started forgetting key points discussed earlier in the session, and I had to repeat myself. It was like having a conversation with someone who could only remember the last few things you said. That’s the effect of reaching the context window limit.
3. Impact on Quality of Responses
As the context window fills up, the model’s responses might start to feel less accurate or relevant, especially if you’re referring to something from much earlier in the conversation. When this happens, you may notice the model beginning to lose track of key details or forgetting specific instructions you gave early on.
For instance, in 4o, if you’re having a complex conversation that spans multiple topics—such as discussing a legal case, transitioning into a philosophical debate, and then circling back to the case—the model might not fully recall the finer points of the initial legal discussion. This doesn’t mean it’s malfunctioning; it’s just a limitation of how much information it can juggle at once.
4. Resetting or Changing Context
If you notice that the conversation starts to drift or lose coherence, it’s often helpful to reset or redefine the context. This could involve summarizing what’s been discussed so far or giving the model a new prompt to reorient the conversation. By doing so, you’re essentially refreshing the context window with new priorities.
Imagine you’re working on a detailed research project with 4o-mini, where you're analyzing different historical periods. Halfway through, you realize that the model is blending details from earlier periods with later ones. At that point, providing a quick recap, like “Let’s focus on 18th-century European history again,” can help steer the conversation back on track.
5. Continuous Interaction
In a single session, the model maintains its context across the back-and-forth exchanges. However, once the session ends—such as when you close a chat window—the context is lost. So, if you start a new session, the model begins from scratch, with no memory of the previous conversation. This can be useful if you want a clean slate, but it also means that long-term memory is not a feature in today’s models.
With o1-preview, developers are exploring ways to extend the context window even further, potentially allowing models to handle even longer conversations without losing track of earlier points. However, the current limits still require strategic management of the conversation flow.
In short, the context window plays a vital role in how well the model can keep track of a conversation. While models like 4o and o1-preview have impressive memory capabilities, they’re not limitless. Once the context window fills up, the model starts to “forget” earlier parts of the conversation to make room for new inputs. Resetting the context or summarizing key points can help manage these limits and keep your interaction on track. Just remember that the model doesn’t have long-term memory—once the chat session ends, it’s like starting over again from scratch.
How To Reset Context Windows
As we've just discussed, language models like 4o and o1-preview have a limited context window—a kind of short-term memory that fills up as the conversation progresses. Once this window gets full, the model may start forgetting earlier parts of the conversation, leading to a gradual decline in the relevance of its responses. But here's the good news: you don’t need to start a new session to reset or realign the context. You can reorient the conversation within the same chat session.
1. Introducing a New System Prompt
One of the easiest ways to reset the model’s context is by providing a new system prompt. For example, if you're discussing a technical topic and notice the conversation veering off course, simply introduce a new prompt like, "Let’s shift back to the original question about data encryption." This signals to the model that it should prioritize this new context over anything else, even if older parts of the conversation are still in the window.
By giving a fresh system prompt, you effectively re-focus the model on a specific subject or task. This can be especially helpful if the conversation has drifted or become bogged down in unnecessary details. Models like o1-preview have been fine-tuned to handle these kinds of shifts smoothly, making transitions between topics more natural and less prone to confusion.
One of my colleagues once used 4o for a long creative writing session, and the model started losing track of earlier plot elements. By introducing a new prompt like, “Can you refocus on the main character’s journey from the beginning of the story?” they were able to get the model back on track without starting the session over.
2. Summarizing Previous Context
If you want the model to retain some elements of the earlier conversation while introducing a new focus, you can try summarizing key points before resetting. This lets you manage the context window more effectively, especially if you’re working on a complex or lengthy task.
For example, imagine you’re brainstorming ideas for a business proposal, and you’ve discussed multiple aspects like market research, budget, and timelines. If the model starts losing track of those discussions, you can say, “Let’s remember that we decided on a $50,000 budget and a launch date in Q2. Now, let’s focus on marketing strategies.” This helps preserve the critical details while shifting the focus to a new topic.
This approach is particularly useful with models like o1-mini, where the context window may be smaller but can still handle relatively complex discussions. By offering concise reminders of what matters, you help the model keep the conversation relevant without overwhelming its memory capacity.
3. Adjusting for Context Limitations
Managing context limitations requires some finesse, especially when dealing with a large amount of information. If you notice the conversation straying or becoming repetitive, it’s often a sign that the model’s context window is getting full. Rather than continuing down a confusing path, re-orienting the conversation with a new focus or summary can save time and frustration.
I once used 4o-mini to help plan an itinerary for a trip. After going through several options, the model started repeating suggestions or offering new ones that didn’t make sense. I realized the context window was overloaded, so I summarized the destinations we’d already decided on and then said, “Let’s focus on restaurants in those areas.” The conversation got right back on track, and I didn’t need to start a new session.
4. No Need for a New Session
One of the great things about these newer models is that there’s no need to start a brand new session just to refresh the conversation. Unlike older systems that might require resetting everything, 4o and o1-preview are designed to adapt on the fly. By simply rephrasing your prompt or summarizing past context, you can continue a conversation with a renewed focus without needing to "start from scratch."
For instance, if you’re deep in a conversation about technology trends and want to shift gears without losing momentum, you can say something like, “Let’s move on from AI now and talk about the future of quantum computing.” The model will understand this as a change in focus and respond accordingly, making the transition smooth and seamless.
Let’s Get Technical…
Even if the term Transformer sounds a bit daunting, it’s not magic—it’s just really clever engineering. Let’s break down how it works, step by step.
1. Tokenization
When you give a prompt to ChatGPT, whether it’s “Explain quantum physics” or “Write a poem about coffee,” the first thing that happens is tokenization. The model doesn’t see your prompt as a sentence or idea the way humans do. Instead, it breaks your input into tokens, which are small chunks of text—typically words or parts of words.
For example, the sentence, “The quick brown fox jumps over the lazy dog,” would be broken down into tokens like [“The,” “quick,” “brown,” “fox,” “jump,” “s,” “over,” “the,” “lazy,” “dog”]. Both the input (what you type) and the output (the response) are processed as these tokens, not full sentences. This process is what allows the model to work efficiently with human language, even when the inputs are long or complex.
Fun fact: o1-preview has a more efficient tokenization process than earlier models. This allows it to handle longer inputs without compromising on speed, which is part of why you can engage in detailed conversations or tasks, like summarizing entire articles or working through complex coding issues, without hitting a wall.
2. Encoding
After tokenization, the model turns these tokens into high-dimensional vectors through a process called embedding. Essentially, these vectors represent the tokens in a mathematical space, allowing the model to understand relationships between words. Think of it like plotting words on a map, where similar words are closer together, and more different words are farther apart.
For instance, the words “dog” and “puppy” would be represented as vectors that are close together, while “dog” and “car” would be much farther apart in the model's understanding. This encoding process helps the model figure out the context of words and how they relate to one another.
I asked 4o-mini to compare cats and dogs, and its ability to link similar traits like "loyalty" and "companionship" between the two was strikingly accurate. This is due to the encoding process, where related words have mathematically similar representations, helping the model create coherent comparisons without losing context.
3. Forward Pass
Once the tokens are encoded into vectors, they pass through layers of neural networks. Each layer analyzes different aspects of the input, from basic syntax to more complex relationships between words. This is called a forward pass, where the input is processed layer by layer, and each layer helps the model "understand" different elements of the text.
In technical terms, 4o and o1-preview have several layers of these networks, with each one adding more depth to how the model processes the text. For example, one layer might focus on identifying the structure of a sentence, while another focuses on understanding the meaning behind certain words or phrases.
4. Attention Mechanism
This is where things get really interesting. One of the biggest breakthroughs with the Transformer architecture is the self-attention mechanism. This allows the model to decide which parts of the input are most important at any given moment. Instead of just processing text linearly, the model can "pay attention" to different parts of the sentence, even if they are far apart.
For instance, in the sentence “The cat, which was sitting on the windowsill, jumped down when it saw the bird,” the model needs to connect the word “cat” with “jumped” and “saw,” even though they are separated by a long phrase. The self-attention mechanism allows it to make these connections effortlessly, improving both coherence and context.
Here’s something neat: In o1-preview, the attention mechanism has been enhanced to handle even more complex sentence structures. This makes the model particularly good at maintaining context over longer interactions, such as answering multi-part questions or engaging in technical discussions.
5. Decoder
Once the model has processed all this information, it moves to the decoder phase, where it starts generating a response. This happens token-by-token. The model predicts the most likely next word based on everything it has processed so far. It’s like solving a puzzle, where each new word helps complete the sentence.
For example, if you ask, “What is the capital of France?” the model uses its knowledge from training to predict the word "Paris" as the most likely and accurate response based on the context and data it has seen.
I once experimented with 4o-mini by asking, “What’s the capital of Italy?” but intentionally left out the last word (“Italy”). The model still completed the sentence with “Rome” because its decoder was able to infer the missing context from the tokens provided. This ability to fill in the blanks is what makes these models so powerful in conversation.
6. De-tokenization
Once the model has generated the next tokens (words), it needs to convert them back into human-readable text. This process is called de-tokenization, and it’s what allows the model to present its response in a way that makes sense to you, the user. What was once just a set of mathematical representations of words is now a coherent, natural-sounding sentence.
For example, the tokens for the sentence “I love pizza” might look like [“I,” “love,” “pizza”] to the model. During de-tokenization, these tokens are combined and presented as readable text.
7. Output
Finally, the model presents its output—whether that’s answering a question, generating a piece of creative writing, or solving a technical problem. And it does all this in a matter of seconds, thanks to the efficiency of the Transformer architecture and its self-attention mechanism.
One notable advancement in models like 4o and o1-preview is their ability to handle complex multi-step reasoning. This is particularly useful in tasks that require logical progression, such as writing a long-form essay, solving a coding issue, or explaining a difficult concept like quantum computing in layman’s terms.
Wrapping It Up: The Future of AI
LLMs have come a long way in a short time, turning what once seemed like sci-fi "Jetsons"-like technology into everyday reality. From answering complex questions to providing inspiration in various ways, these AI systems have reshaped how we interact with technology and the web. While OpenAI’s models are at the forefront, it’s worth recognizing that they’re part of a much bigger picture. Companies like Google, Meta, and Anthropic are pushing boundaries too, each with their own take on what AI can do.
What ties them all together is the shared goal of building smarter, faster, and more efficient models. The world of AI is moving fast, and while it can be hard to keep up, one thing is clear: this technology is only going to get better and more integrated into how we live and work. OpenAI, alongside other major players, is shaping a future where human-AI collaboration feels seamless and natural. So whether you’re just curious or actively exploring how to use these tools, the future of AI holds plenty of exciting developments—ones we’re only beginning to scratch the surface of.
