How Large Language Models Are Built

Modern AI tools like GPT, Gemini, Claude and others look like magic from the outside. They can write code, answer questions, explain math, and even chat like a human. But behind that “magic” is a long, expensive, and very technical process.

In this guide, we’ll walk through a simple 5-step process of how large language models (LLMs) are built:

1. Data curation

2. Tokenization

3. Model architecture

4. Training at scale

5. Evaluation and alignment

You don’t need a PhD to follow along. We’ll keep the language simple and focus on the main ideas.

Step 1: Data Curation – Getting the Right Data

Large language models learn from data. A lot of data. Companies spend hundreds of millions of dollars on compute to train models like GPT-5 or Gemini Ultra. But before they can train, they need huge, high-quality datasets. That’s where data curation comes in.

Data curation has several parts:

1.1 Data collection

The first thing is collecting text at massive scale. This might include:

• Web pages

• Books

• Scientific papers

• Code repositories

• Wikipedia

• Forums and Q&A sites

• Special datasets from research groups

Some open datasets (like FineWeb on Hugging Face) have billions of rows. Modern models are trained on trillions of tokens (a token is a small chunk of text, like a word or part of a word).

Why so much data?

There is a concept called scaling laws. In short:

• More data → lower loss (better learning)

• More compute → lower loss

• Bigger models (more parameters) → lower loss

So, bigger and better models usually require more data, more compute, and more parameters.

1.2 Cleaning and filtering

If you just “scrape the internet” and throw everything into training, you get garbage.

Garbage in → garbage out.

So after collecting raw data, teams must:

• Remove HTML tags (<div>, <script>, etc.)

• Remove boilerplate (menus, ads, repeated templates)

• Filter out spam, low-quality pages, and obvious nonsense

• Remove illegal or harmful content

• Remove private or sensitive data (personally identifiable information, medical details, etc.)

This is a huge job. It requires:

• Automated filters

• Heuristics (rules)

• Machine learning classifiers

• Human review for sensitive cases

1.3 Deduplication

The internet contains many copies of the same information. Example: search for “merge sort code” and you’ll see almost the same algorithm repeated across thousands of sites.

If the model sees the same text many times, it wastes capacity and can overfit. So teams remove duplicates at several levels:

• Exact duplicates: Use hashing algorithms like SHA-1 or MD5.If two pages have the same hash, they’re exactly the same.

• Near duplicates: Maybe the text is 95% the same, just with some edits. Algorithms like MinHash, SimHash, and locality-sensitive hashing (LSH) help find these.

• Semantic duplicates: Different words but the same meaning. For example, “What is merge sort?” and “Explain the merge sort algorithm.” You don’t want 50 copies of the same explanation.

• Code deduplication: Same logic, different variable names.“mergeSort(arr)” vs “sortMerge(list)” – same algorithm, different labels.

1.4 De-identification and safety

Modern LLMs must respect user privacy and safety. So teams:

• Remove names, email addresses, phone numbers where possible

• Strip out sensitive personal details

• Filter out toxic, hateful, or violent content

• Tag or remove unsafe examples

This is also where safety filters and policy rules come in.

1.5 Special datasets for fine-tuning

Raw internet text is not enough to create a helpful chatbot.

You also need curated, high-quality examples where:

• A user asks a question

• An expert gives a good answer

These examples are used later for fine-tuning and instruction following.

They are often created by:

• Human annotators for general tasks (e.g. “Write a polite email”, “Explain photosynthesis to a 10-year-old”)

• Domain experts for specialized fields (law, medicine, finance, etc.)

There are companies that specialize in this human-labelled data. They provide:

• High-quality question–answer pairs

• Safety labels (e.g. “This response is toxic / not allowed”)

• Preference labels (which answer is better?)

All of this is part of data curation, the foundation of the whole process.

Step 2: Tokenization – Turning Text into Numbers

Computers don’t understand text. They understand numbers. So before training, all text must be turned into a numerical form. This happens in two main steps: tokenization and embeddings.

2.1 What is a token?

A token is a small unit of text. It could be:

• A full word (cat, house)

• Part of a word (ing, tion)

• A punctuation mark (. or ,)

• Sometimes even a whole phrase, depending on the tokenizer

Example sentence:

“I am eating paratha.”

A tokenizer might split it into:

• I

• am

• eat

• ing

• par

• atha

(or different chunks, depending on the algorithm).

Each token gets mapped to a token ID, which is just a number.

2.2 How tokenization works (simple view)

Many models use techniques like Byte Pair Encoding (BPE) or similar algorithms. The idea is to:

• Start from characters or small pieces

• Merge frequent patterns into tokens

• Build a vocabulary that balances flexibility and efficiency

Some newer architectures skip complex tokenization and operate directly on raw bytes (like UTF-8). This avoids building a language-specific tokenizer but needs more careful model design.

2.3 From tokens to embeddings

Once you have token IDs, the model converts each ID into a vector of numbers called an embedding.

• Token → ID → embedding (a list of numbers)

Embeddings are the model’s way of representing meaning in a continuous space. Words with similar meanings end up with similar embeddings.

Step 3: Model Architecture – Designing the Brain

Now we have data and tokens. Next we need to design the architecture, the “brain” of the model. Most modern LLMs are based on the Transformer architecture.

3.1 The transformer at a glance

A transformer is a deep neural network designed to handle sequences of tokens. Its key components include:

• Embedding layers

• Self-attention blocks

• Feed-forward layers

• Positional encodings (or similar mechanisms)

At the heart of the transformer is the attention mechanism.

3.2 Attention: understanding context

Consider the word “bank” in these two sentences:

1. “He sat on the bank of the river.”

2. “She deposited money in the bank.”

The word “bank” has a different meaning in each sentence. How does the model know which meaning to use?

The attention mechanism lets each word look at other words in the sentence and decide which ones are important for understanding the context.

• In sentence 1, “bank” attends to “river”.

• In sentence 2, “bank” attends to “money” and “deposited”.

This context-aware process is what gives transformers their power.

3.3 Making attention faster and cheaper

Training huge models is very expensive. So researchers keep inventing improvements to make attention more efficient, such as:

• Flash attention

• Sparse attention

The goal is always the same: reduce memory and compute cost while keeping accuracy high.

3.4 Positional information

Transformers also need to know the order of tokens (word position matters).

They use things like:

• Positional embeddings

• Rotary position embeddings (RoPE)

• Other advanced schemes

These tell the model “this is the 1st token, this is the 2nd, …” so it can handle sequences correctly.

3.5 Scaling tricks: Mixture of Experts, activations, optimizers

To build bigger and faster models, teams also use:

• Mixture of Experts (MoE): Different “experts” (sub-networks) handle different parts of the input. Only a few experts are active at a time, so you get higher capacity without using all parameters for every token.

• Modern activation functions: Instead of classic ReLU or sigmoid, newer models use things like GELU or SwiGLU to improve performance and stability.

• Advanced optimizers: Traditional neural nets often use Adam. Large-scale training pushes new optimizers (like Muon and others) to handle massive parameter counts more efficiently.

All these architectural choices aim to answer one question: How can we get the most intelligence per dollar of compute?

Step 4: Training at Scale – Teaching the Model

Now comes the most expensive part: training.

We have:

• Huge datasets (trillions of tokens)

• Huge models (hundreds of billions or even trillions of parameters)

• Huge GPU clusters

Training a frontier model can take months and cost tens or hundreds of millions of dollars.

4.1 What are parameters?

In a neural network, each connection has a weight (a number). These weights are called parameters.

In a tiny network, you might have 100 parameters. In GPT-level models, you might have hundreds of billions or more.

Training means:

• Feed the model examples

• Compare its prediction to the correct answer

• Adjust the weights (parameters) to reduce error

This process is repeated over and over, across massive datasets.

4.2 Massive compute and GPU farms

To do this at LLM scale, companies build huge data centers filled with GPUs or specialized chips.

• Thousands of GPUs work in parallel

• Data is split across machines

• Gradients are synchronized

• Everything must be carefully optimized

Engineers write low-level code (for example, with CUDA on NVIDIA GPUs) to squeeze out every bit of performance.

4.3 Sub-steps of training

Training LLMs is not just “one big training run.” It usually has several phases:

4.3.1 Pre-training

Goal: predict the next token.

For example, given:

“Sure, we can”

The model learns that the next token might be “do,” not “car” or “pizza”.

Pre-training teaches:

• Grammar and language structure

• World knowledge from the text

• Basic reasoning patterns

At this point, the model is not yet a nice chatbot. It’s more like a general text prediction machine.

4.3.2 Mid-training (optional but common)

Here, the model might be trained on:

• Longer context documents

• More complex instructions

• Reasoning-focused data

This phase improves:

• Memory over long sequences

• Coherence

• Reasoning and planning

4.3.3 Supervised fine-tuning (instruction tuning)

Now we want the model to behave like a helpful assistant, not just complete random text.

We use question–answer pairs or instruction–response datasets. For example:

• Instruction: “Give three tips for staying healthy.”

• Output: “Eat a balanced diet, exercise regularly, sleep enough.”

• Instruction: “Identify the main conclusion from this medical report.”

• Output: A concise summary.

This stage:

• Teaches the model to follow instructions

• Makes it answer questions directly

• Shapes it into something like ChatGPT rather than just a raw text generator

4.3.4 Preference fine-tuning (RLHF, DPO)

Even after supervised fine-tuning, not all answers are equally good.

We want:

• Helpful

• Honest

• Harmless

• Polite

• Aligned with user expectations

Techniques used here include:

• Reinforcement Learning from Human Feedback (RLHF):Humans compare two model outputs and say which is better.The model is then updated to prefer those “better” answers.

• Direct Preference Optimization (DPO):A more direct way to learn from preference data without full RL.

This phase helps shape the tone, style, and safety of the model.

4.3.5 RL with verifiable rewards

RLHF has limits:

• Human judgments can be biased or inconsistent

• It doesn’t scale easily to billions of examples

• Some tasks have clear right/wrong answers that don’t need human opinion

For those tasks, we can use verifiable rewards. Example: code generation.

Process:

1. Model writes a piece of code.

2. We run automated tests or try to compile it.

3. If it passes, give a high reward. If not, give a low reward.

Here, the “judge” is objective:

• Tests pass → good

• Tests fail → bad

This allows the model to improve in a more reliable, scalable way for certain tasks.

Step 5: Evaluation – Is the Model Actually Good?

After all this training, we still need to ask:Is the model good enough to use?

Evaluation has two big parts:

1. Technical quality

2. Safety and alignment

5.1 Technical benchmarks

Models are tested on many benchmarks:

• General knowledge and reasoning

• Coding tasks

• Math problems

• Domain-specific exams (medicine, law, etc.)

These benchmarks help answer:

• Can it solve exam-style questions?

• Can it write working code?

• Can it follow complex instructions?

5.2 The challenge: non-determinism

LLM outputs are probabilistic, not fixed.

If you ask the same question multiple times, you might get slightly different answers.

So traditional “exact match” testing (like in normal software) doesn’t fully work. Instead, teams use:

• Semantic similarity: Compare the meaning of model output to the expected answer using metrics like cosine similarity between embeddings.

• Thresholds: For example: “If similarity > 80%, count as pass.”

• LLM-as-a-judge: One model generates the answer, another model (or a human) judges if it is correct.

• Code tests: For coding tasks, run test suites, not just compare text.

5.3 Safety and policy checks

The model must also:

• Refuse to produce dangerous content (e.g. “How do I build a bomb?”)

• Avoid hate speech or abuse

• Respect privacy

• Handle sensitive topics carefully

Safety evaluation includes:

• Red-team testing (trying to break the model’s safety rules)

• Automatic filters

• Human review for edge cases

Only after passing these evaluations does a model become ready for deployment in products like chatbots, APIs, or internal tools.

Final Recap: The 5-Step Process

Let’s quickly recap the whole lifecycle:

1. Data curation

   • Collect massive text and code datasets

   • Clean, filter, de-duplicate, and de-identify data

   • Build special instruction and expert datasets

2. Tokenization

   • Split text into tokens

   • Map tokens to IDs

   • Turn IDs into embeddings (numbers)

3. Model architecture

   • Design a transformer-based neural network

   • Use attention, positional encodings, scaling tricks, and efficient activations/optimizers

4. Training at scale

   • Pre-train on huge datasets for next-token prediction

   • Mid-train and scale for longer context and reasoning

   • Supervised fine-tune with instruction data

   • Preference tune with RLHF / DPO

   • Use RL with verifiable rewards for tasks like code

5. Evaluation

   • Test on benchmarks (knowledge, reasoning, coding, math)

   • Check safety and alignment

   • Use semantic matching, test suites, and LLM-as-judge approaches

The process is evolving all the time. New tricks, better architectures, smarter training methods, and more robust safety tools keep appearing. But most large language models today follow this general 5-step path. That’s the big picture of how the AI systems you see today are actually built.