How to Build an LLM: A Simple Guide for Beginners

A simple explanation of how to build an LLM (Large Language Model) like ChatGPT, Claude, and Gemini: the five stages, the cost, the key terms, and three realistic paths for businesses to use AI without building one from scratch.

Adriel Anderson· Founder · Engineering· June 08, 2026· 9 min read

Every day, millions of people type questions into ChatGPT, Claude, or Gemini and get answers that feel like talking to a human. But have you ever wondered how an AI like that is actually built? Is it only for tech giants, or do ordinary businesses have a way in too? This article explains how to build an LLM in plain language, without math formulas, so even non-technical readers can understand the big picture.

LLM stands for Large Language Model. It is a type of artificial intelligence trained to read enormous amounts of text and then learn to guess the next word in a sentence. From that simple ability to guess words come things that look magical: answering questions, writing articles, generating code, and translating languages.

Quick summary

An LLM is an AI model that learns from large amounts of text to guess and generate words.
Building an LLM from scratch goes through five major stages: collect data, tokenization, pre-training, fine-tuning, and alignment.
Training a world-class LLM from scratch is very expensive: it needs thousands of graphics cards (GPUs) and costs that can reach tens of millions of US dollars.
For almost every business, the realistic path is not building from scratch, but using a ready-made model API, fine-tuning an open-source model, or applying RAG.
You do not need to be a math expert to start using an LLM inside a product or business.

What is an LLM, explained with an analogy

Imagine a student who reads almost the entire world library: books, articles, forums, documentation, and web pages. The student does not memorize everything word for word, but captures patterns: which word usually follows another, how sentences are structured, and how ideas are explained. An LLM works in a similar way.

When you type a question, an LLM does not look up an answer in a database like Google. It guesses, one small piece of a word at a time, the most reasonable arrangement of words as an answer based on the patterns it has learned. Because those patterns are so rich, the result often feels intelligent and natural.

The five major stages of building an LLM

1. Collecting and cleaning data

It all starts with text. A team gathers massive amounts of text from the internet, books, code, and other sources, then cleans it: removing duplicates, low-quality content, sensitive personal data, and harmful material. Data quality matters far more than sheer quantity. The old principle still holds: garbage in, garbage out.

2. Tokenization: breaking text into small pieces

Computers do not read words the way humans do. Text is broken into small pieces called tokens. A token can be a short word, part of a word, or a punctuation mark. For example, a longer word can be split into several tokens. Models work with tokens, not letters, and this is the unit you are charged for when using a paid LLM.

3. Pre-training: the heaviest learning stage

This is the heart of building an LLM. The model is given billions of sentences and asked to guess the next word over and over, hundreds of billions of times. Each time it guesses wrong, the numbers inside the model are adjusted little by little. This process runs for weeks or months across thousands of graphics cards (GPUs) working in parallel. This stage is what makes training a frontier model so expensive.

The result of pre-training is a base model that is very good at language but does not yet know how to be polite or follow instructions neatly. It is like a brilliant student who has not been taught the etiquette of answering.

4. Fine-tuning: teaching it to follow instructions

Once the base model exists, it is refined with high-quality examples of conversations and instructions: questions paired with their ideal answers. This is where the model learns to become an assistant that answers questions rather than just continuing text. Fine-tuning is also used to make a model an expert in a specific field, such as law, healthcare, or a company customer service.

5. Alignment: teaching it which answers are good

The final stage makes the model safer and more helpful. A popular technique called RLHF (Reinforcement Learning from Human Feedback) works like this: humans rate several model answers, deciding which is better and more polite. The model learns from those ratings so its answers match human expectations better, stay more honest, and refuse harmful requests. This is the big difference between a raw model and a comfortable AI assistant like Claude or ChatGPT.

How much does it cost and how long does it take to build an LLM from scratch?

For a world-class (frontier) model, the reality is demanding: it needs thousands of expensive GPUs, a team of specialist researchers, massive amounts of data, and computing costs that can exceed tens of millions of US dollars for a single training run. This is why only a handful of large companies build giant models from scratch.

But building a small model for learning or experimentation is far more affordable. Many developers train a mini model on a single computer just to understand the concept. The scale is very different, but the five-stage principle above stays the same.

Three realistic paths for businesses (without building from scratch)

01Use a ready-made model API. You connect your product to a model such as Claude, GPT, or Gemini through an API and pay per usage (per token). This is the fastest path, with low upfront cost, and it suits chatbots, document summaries, or in-app assistants.
02Fine-tune an open-source model. Open models such as Llama or Mistral can be downloaded and refined with your specific data. This fits when you need full control, data privacy, or a very specific answer style.
03Use RAG (Retrieval-Augmented Generation). Instead of retraining the model, you connect it to your own documents (FAQ, product catalog, standard operating procedures) so it answers based on your company data. This is the most popular and cost-effective way to build an AI that understands your business.

Key terms you will often hear

Token: a small piece of text the model processes; the basis for pricing on paid services.
Parameter: the numbers inside the model that store patterns; large models have billions to trillions of parameters.
Context window: how much text the model can read at once in a single conversation.
Hallucination: when the model answers confidently but incorrectly. Always verify important information.
Prompt: the instruction or question you give the model. A clear prompt produces a better answer.
RAG: a technique that connects the model to your data sources so answers stay accurate and relevant.

When does your business need to think about an LLM?

An LLM makes sense when there is a lot of text to process repeatedly: answering customer questions, summarizing documents, sorting feedback, drafting content, or helping an internal team find information quickly. If that work consumes many human hours and follows a repeating pattern, that is where AI can deliver real value.

On the other hand, if your need is rare, very simple, or demands absolute accuracy with no tolerance for error, first consider whether an LLM is truly the right solution. Good technology still has to fit the problem.

Closing

Building an LLM from scratch is indeed a massive undertaking, but understanding how it works is not as complicated as it seems: collect data, break it into tokens, train it to guess words, refine it with instructions, then align it to be safe and helpful. The hardest and most expensive part is training, and fortunately that part has already been done by large companies whose models we can use directly.

At Respawn Society, what matters more is not joining the race to build giant models, but helping businesses use LLMs appropriately: connecting them to real products, data, and workflows so they genuinely save time and add value. If you are curious how AI could be applied in your business, we are happy to discuss it.

— TagsLLMhow to build an LLMartificial intelligenceAI for businessmachine learningfine-tuningRAG