How Large Language Models (LLMs) Like ChatGPT Actually Work: 7 Steps

Behind the illusion lies structure — LLMs turn language into logic.

Inside LLM Technology: Understanding the Process

• LLMs don’t “understand” — they predict: Tools like ChatGPT generate language by recognizing patterns in massive datasets and predicting the next word, not by storing facts or forming opinions.
• Training isn’t enough — tuning is key: From instruction fine-tuning to reinforcement learning from human feedback (RLHF), the most useful models undergo multiple refinement stages to behave helpfully.
• Even top models can hallucinate: Without live data access, LLMs sometimes generate confident but incorrect answers—understanding this helps users prompt and verify more effectively.

Large Language Models, or LLMs, are now central to AI applications that generate text, answer questions, write code, and summarize information. These systems power tools like ChatGPT, Claude, and Gemini. But what exactly are they, and how do LLMs work? What should you know before creating one? This article will walk through the fundamentals, give practical insights, and provide examples you already know.

What Are LLMs

Large Language Models function as AI systems that understand and produce human language. They learn how words are used in context by training on large datasets such as books, websites, articles, and transcripts. Their main function is to predict the next word in a sentence. Over time, this simple process allows them to create complex outputs that sound coherent, useful, or even creative. If you have ever wondered how do LLMs work behind the scenes, the answer starts with how they learn from patterns in massive volumes of text.

They do not learn facts the way humans do. Instead, they track word patterns and context. They do not have intentions or knowledge in the human sense. Their skill lies in high-level pattern matching and sequence prediction.

Common LLMs You Know

Before going deeper into how these systems work, it helps to recognize a few popular examples. Each of the following models represents a different approach or philosophy in LLM development. Some serve broad public use, while others focus on research or experimentation.

ChatGPT by OpenAI

First released in late 2022, ChatGPT is based on the GPT-3.5 and GPT-4 architecture. It uses reinforcement learning from human feedback (RLHF) to provide helpful and aligned responses. It is one of the most popular LLMs available and supports a wide range of applications from casual chat to professional writing.

Gemini by Google DeepMind

Formerly known as Bard, Gemini is Google’s flagship conversational AI system. It integrates Google’s search data and optimizes information tasks. It aimed to compete directly with ChatGPT.

Claude by Anthropic

Claude was introduced by Anthropic, a company founded by former OpenAI employees. It emphasizes alignment and safety, aiming to produce thoughtful and harmless responses. Claude uses a similar underlying transformer architecture with training methods focused on constitutional AI.

LLaMA by Meta

Meta’s LLaMA (Large Language Model Meta AI) is an open-weight model made available to researchers and developers. It gained popularity for its flexibility in custom model development. In 2023, developers released LLaMA models, and many independent AI projects continue to use them.

Mistral and Other Open Models

Smaller labs like Mistral and EleutherAI have released lightweight open models focused on performance and accessibility. These often serve as the backbone for experimental or self-hosted AI systems.

How Do LLMs Work?

Understanding how LLMs generate fluent, context-aware responses means looking at how they are trained, structured, and refined. Each model, whether it’s ChatGPT, Claude, Gemini, or LLaMA, follows similar phases with unique adjustments.

Step 1: Data Collection and Preprocessing

Before any training begins, LLMs need enormous amounts of text data. This material is gathered from a wide range of sources: books, Wikipedia articles, academic papers, social media posts, technical documentation, forums, news websites, and open-source code repositories. Understanding how do LLMs work begins with recognizing the importance of this diverse training data. The goal is to expose the model to a wide variety of vocabulary, sentence structures, topics, and styles.

Once collected, the data is cleaned and formatted. This includes removing:

• Duplicate or near-identical entries
• Irrelevant or nonsensical content
• Text with broken formatting or unreadable characters

Many models apply further filtering to remove biased, offensive, or low-quality material. Some also exclude copyrighted content unless permission has been granted.

Examples:

• OpenAI uses a combination of internet-scale datasets and licensed data, including books and code.
• Meta’s LLaMA limits its data to publicly available sources.
• Google’s Gemini supplements web data with proprietary search logs and structured factual databases.

This initial step shapes everything that follows. The quality and diversity of the data have a direct impact on how versatile or biased a model becomes.

Step 2: Pretraining with Next-Word Prediction

Once the data is ready, the model begins pretraining. The task is simple on paper: given a sequence of words, guess what comes next. This is called next-word prediction or language modeling.

For example, the model might see:

• “The Eiffel Tower is located in [blank]”
  And it learns that “Paris” is a likely next word based on its training.

This process is self-supervised. That means there’s no need for humans to label the data—each word naturally follows another. As the model is exposed to more and more text, it gradually improves its understanding of:

• Grammar and syntax
• Word meanings and common pairings
• Basic facts and associations
• Tone, formality, and context shifts

At this stage, the model learns how language behaves. To fully answer how do LLMs work, it’s important to note that this phase does not teach the model how to follow instructions, answer questions, or interact with users in a helpful way, which comes later.

Step 3: Architecture and Attention Mechanism

The structure powering nearly all LLMs today is called the transformer. Introduced in 2017, this architecture processes entire word sequences in parallel instead of one token at a time. Its biggest innovation is the attention mechanism, which helps the model focus on the most relevant words in a sentence when generating output.

If you ask, “When did she win the award, and why was it important?” the model must figure out who “she” refers to and what context matters. Attention helps make these decisions.

Transformers are also scalable. This architecture allows models like GPT-4 or Gemini Ultra to reach hundreds of billions of parameters while maintaining efficient training and inference performance.

Step 4: Instruction Fine-Tuning

Pretrained models are fluent but not helpful. Without further tuning, they might respond to “What is 2 + 2?” with “What is 3 + 3?” because they’re trained to continue sequences, not follow directions.

Instruction fine-tuning fixes this. Developers provide the model with thousands of example prompts and human-written ideal answers. This teaches the model how to respond to commands and complete specific tasks.

Examples:

• ChatGPT was refined using datasets of prompt-response pairs to behave like a conversational assistant.
• Claude uses a method called Constitutional AI, where it’s trained on written principles about ethics and behavior, rather than relying only on human examples.

This stage dramatically improves a model’s ability to answer questions, complete tasks, and adapt to user input.

Step 5: Reinforcement Learning from Human Feedback (RLHF)

After instruction tuning, developers add another layer of refinement. Humans rank multiple model responses for the same prompt. The model then learns to prefer higher-ranked responses in future predictions.

This method, known as Reinforcement Learning from Human Feedback, adds alignment. It helps the model prioritize clarity, helpfulness, and safety.

• ChatGPT saw major improvements after RLHF was introduced in GPT-3.5 and expanded in GPT-4.
• Claude uses RLHF selectively, relying more on its internal rule system.
• Smaller or open models often skip RLHF due to the high cost of human annotation.

RLHF helps reduce rambling, contradictions, or off-topic replies. It gives models a sense of how people expect a helpful assistant to behave.

Step 6: Deployment, Sampling, and Prompt Engineering

Once deployed, an LLM generates answers by predicting one word at a time. For each prediction, it uses the entire previous context to make the next choice. This is where sampling strategies come in:

• Top-K or Top-P (Nucleus) Sampling: The model selects from the most likely group of words instead of always choosing the top one. This adds variety and avoids repetition.
• Temperature Setting: A higher temperature makes responses more creative and surprising. A lower one keeps the output focused and safe.

At this stage, user interaction matters. The same question, phrased two different ways, can lead to very different answers. That’s why prompt engineering, the art of writing better instructions, is a critical part of getting high-quality results.

Step 7: Grounding with External Tools (Optional)

Some LLMs go one step further by connecting to live data sources or tools. This helps them stay current and reduce hallucinations.

Examples of grounding:

• Bing Chat combines GPT with real-time search to pull in up-to-date facts.
• Some models integrate with calculators, code execution environments, or document databases.
• Enterprise tools like ChatGPT Enterprise or Google’s Gemini for Workspace allow grounding in company-specific data through plugins and APIs.

These integrations are optional, but they extend what LLMs can do, from accurate fact retrieval to real-time task execution.

Why It Sometimes Gets Things Wrong

Large Language Models generate responses by predicting what words are likely to follow a given input. They do not have a live connection to the internet or a fact-checking system built in. Everything they produce is based on patterns they learned during training, which often ended months or even years before the model is released.

This means that:

• They cannot access current events. If you ask who won a recent election or what the weather is today, the model will not know unless it’s been given that information in your prompt.
• They may guess if uncertain. When the model encounters unfamiliar questions, especially about niche topics or dates beyond its training data, it will try to generate a plausible-sounding answer. These guesses can be wrong, but are written with high confidence. This is referred to as hallucination.
• They rely on probability, not verification. The model’s goal is to continue a sentence in a way that fits common usage. It does not “know” what is true; it only knows what would usually come next in similar situations seen during training.

To reduce these issues, some models include external systems:

• Search-augmented LLMs, like Bing Chat, perform a real-time web search and pass relevant snippets to the model as part of the input.
• Plugins and retrieval tools, like those used in some versions of ChatGPT, allow the model to access databases, documents, or APIs to ground its answers in real data.

Still, even with these tools, hallucinations can happen if the prompt is vague, misleading, or if the supplemental context is inaccurate or incomplete. That’s why high-stakes tasks often require human review, external validation, or additional logic layered on top of the model’s output.

Tips on Making Your Own

If you want to train or fine-tune a language model, you don’t have to start from scratch. Many tools and open-source models are available. Here are practical tips that can help guide early development.

• Use open-source models like LLaMA or Mistral
• Choose a domain (legal, education, creative writing)
• Start with a small version and scale gradually
• Clean your training data thoroughly
• Focus on one use case, not everything
• Test for bias, inaccuracy, and repetition
• Fine-tune using high-quality examples
• Use strong GPUs or cloud services
• Monitor responses constantly during training
• Provide clear input prompts during testing

How Small Business Owners Can Turn LLM Knowledge Into Real Results

LLMs have changed how we use language online. Though not magic or sentient, they are complex prediction engines trained on text. The better we understand their structure and limits, the more we can use them effectively or build them responsibly. If you want to create or apply these systems wisely, start by asking how do LLMs work at each stage. Whether you use a model like ChatGPT or train your own, knowing how they work is the first step toward using them with purpose.

Tired of paying for bloated SaaS tools you barely use?

Let BotHaus build you a lean, custom AI assistant that actually does what your business needs—no monthly fees, no fluff.
Start your build for free today. Own your automation.

Frequently Asked Questions