What is LLM Training Data?

When people talk about LLM training data, it can mean different things. You could be talking about training an LLM system to know how to predict and generate language by learning patterns from the massive amount of texts that you provide. You could also be talking about taking an existing model and shaping how it behaves with a smaller dataset that is more focused on the goal you want the LLM to achieve.

These two are not the same and are equally important when training production-level LLMs. The first action is called pretraining, it is usually the first step and it requires a lot of data (trillions of tokens) to train the model on how to understand general language behavior. The second one is called fine-tuning, this usually happens after pretraining, and it uses significantly less data but the data must be of high value and focused on a specific task, domain, or style. 

In this article, we will explore how to train data for LLMs, where that data comes from, the type of data you can train them with, and the practical choices you can make when you’re working with custom, open-source, and synthetic training data.

Determining Data Volume: Parameters, Tokens, and Scaling Laws

The amount of data you need to effectively train an LLM depends on certain factors like the model size, the budget you are working with, and your training goals. You cannot quantify the amount of data you need because more data doesn’t automatically produce a better model. 

A more practical way to think about training data for LLMs is to think about the cost rather than raw data size. Every token you process costs money, and every increase in model size multiplies that cost. What you need to think about is not how many data tokens you need but how many data tokens you can afford to use to effectively train your LLM. 

The first thing you need to do is decide what model size you want to train. The model size you choose will determine the number of tokens you will need, the estimated budget that will cover the operation, and the quality ceiling you can realistically reach. 

For a given budget, there is a right model size, and a right amount of data that will optimally train your LLM. If you go beyond that point, you will simply waste money without improving the training result.

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Research from the 2022 Chinchilla paper shows that there is a fixed relationship between the training budget, model size, and the amount of data you require to train your model. Researchers suggest that you can train an LLM to an optimum level when the number of tokens is roughly 20 times the number of model parameters.

For example, if you train a 3-billion-parameter model like Meta’s OPT-2.7B, you will need about 60 billion tokens to train it optimally. Anything below that will not be enough to train the model to its best capability and anything far above it will just be a waste of money. Meta’s LLaMA-2 model that has 70-billion-parameters also followed this same logic and was trained on over 1.4 trillion tokens. That scale only makes sense because the model size and budget supported it.

In the original GPT-3 paper (Brown et al., 2020), OpenAI introduced a 175-billion-parameter model trained on approximately 300 billion tokens drawn from licensed data, data that humans created, and open-source text datasets. GPT-3 achieved strong performance across many language tasks. However, later research suggested that it couldn’t perform optimally because of its training setup. According to the Chinchilla scaling findings, a 175-billion-parameter model would require closer to 3.5 trillion data tokens (about 20× the parameter count) to reach optimal efficiency.

Data Sources for LLM Training

There are a couple of intelligent large language models out there today like the GPT-5.2 series, Claude 4.5, Gemini 3, etc., and you might be wondering where they get so much information from. These systems do not just “know” things, they learned patterns, adapted to language structure and have understood inter-word relationships from massive amounts of LLM training data collected over time. The training data you use depends on your idea of scale, what you have a license to access, and the decisions you and your team makes long term. 

Most teams begin with large open-source datasets like: 

• The Pile: An 886 GB dataset that contains English text obtained from Stack Exchange, Wikipedia, arXiv, Github, Youtube subtitles and other sources across the web.
• Red Pajamas dataset from TogetherAI: A 1.2 trillion token dataset obtained from the data they used to train the LLaMa model i.e. Common crawl pages, Github, C4 dataset, arXiv, etc.
• Falcon RefinedWeb: A 5 trillion token dataset extracted from carefully filtered and deduplicated Common Crawl web data, with a 500 billion token RefinedWeb subset publicly released.
• Dolma: A 5.9 trillion token dataset built for the OLMo 3 models. It includes a 100 billion-token mid-training mix focused on math, code, reasoning, and QA sessions.
• StarCoder: 783GB dataset that was used to train the StarCoder model obtained from over 80 programming languages, GitHub issues, Jupyter notebooks, and Git commits.

These datasets grant your LLM access to scraped content from the web, open repositories, Wikipedia, GitHub, and other openly accessible sources. They are large, free to download, and you can start using them to pre-train models today.

You need to have access to at least hundreds of thousands to millions of tokens for your model to have any sort of meaningful behavior. The training data you need quickly grows into trillions of tokens in larger scale models, that is why companies nowadays train their models with data gotten from aggressive web scraping combined with the open-source datasets. Scraping is one of the few ways you can get access to the volume of data you need to effectively train an LLM.

Choosing the Right Data Strategy for Your Use Case

The data you use to train a Large Language Model depends on what you want to use it for. There is no single dataset that works for every use case so before collecting LLM training data, you need to clearly define your training goals and the type of output you expect from the model. 

Most LLMs rely on datasets that are mostly text obtained from different sources around the web like Common Crawl, books, wikipedia, academic papers from arXiv, source code from GitHub, and Q&A data from platforms like Stack Exchange, Stack Overflow etc. You can see some of  these sources appear frequently when you prompt popular multifunctional LLMs like ChatGPT that have been trained with trillions of words and tokens. These types of models can perform different functions because the developers divide the datasets into proportions based on how relevant they are for the task you want the model to perform.

How to Decide Which Data to Use

The best way to choose LLM training data is to match the data directly to the task you want the model to perform:

• If you want a general-purpose LLM, get your data sources from text on the web, books, encyclopedias, wikipedia and the likes. This will ensure your model can handle a wide range of topics and everyday language.
• If you want an LLM model that performs specific tasks, then you need to tailor your data sources to your specific needs for the LLM. For instance if you are building a model for legal research then you get data from court opinions, statutes, contracts, internal legal documents, etc. If you are building a market trade analysis model, you can get sources from historical trade records, internal transaction logs, market reports, and structured data that show pricing, volume, and timely trends. This also applies to customer support models, medical models, etc.
• If you want a model that can code in different programming languages, you need to get your sources from high quality code data on platforms like GitHub, well-documented production codebases with clear commit history, official SDKs and libraries, and technical discussions where developer communities solve real problems and solutions. These data sources will help your model learn code syntax, structure and programming patterns for different languages. You also need to mix natural language in here too so your model can also explain the code it writes and follow technical instructions.
• If you want an LLM that comfortably communicates in another language like French or German, then you need to prioritize high quality French or German datasets or any other language you are working with from relevant sources.
• If you are working with a limited budget, test your model with smaller datasets first. You can decide to scale up if the results you get and the results you want justifies the additional cost you will have to incur. 

The Economics of LLM Training: Compute, Hardware, and Labor

Training an LLM is not cheap, in fact it is one of the biggest expenses in building modern AI systems. Let’s talk about the elements that make up the total cost of LLM training.

1. Compute (GPU Hours)

One of the biggest expenses usually comes from the GPUs (graphics processors) used to do the heavy math that trains the model. Your model might require thousands of GPUs running for weeks or months for effective training. Each hour the GPU runs costs money, and when you multiply that across thousands of machines, that number can grow exponentially.

Statista’s Katharina Buchholz reported in 2024 that earlier models were much cheaper to train, the GPU hours spent training the GPT-3 (2020) model cost OpenAI between $2 million to $4 million. Google’s PaLM model (2022) reportedly cost between $3 million to $12 million in compute alone. Now because of the rapid adoption of these LLMs around the world and the number of computations needed to make the model properly interact with humans, the cost of training these models have now skyrocketed. 

In 2023, OpenAI’s CEO Sam Altman stated that training the GPT-4 model would cost over $100 million in compute costs alone. Further research also showed that the Gemini model cost up to $191 million to train, and that is before you consider staff salaries.

2. Hardware and Infrastructure

Apart from GPU hours, you will also need the following:

• Thousands of GPUs or specialized chips (like TPUs)
• Networking gear to connect all the machines
• Storage systems for massive datasets and,
• Systems to manage power and cooling

NVIDIA H100 Price Guide shows that a single NVIDIA H100 GPU costs around $25,000, while newer versions like the H200 can cost close to $39,000 each. Your model will ideally require hundreds or even thousands of these GPUs working together to be able to train it with enough hours.

The GPU price is only part of the expense. Each unit also needs power, cooling, networking equipment, and server infrastructure. These additional setup costs can add anywhere from $5,000 to $50,000 per GPU, depending on the scale of the operation.

3. Obtaining and Preparing Data

You also need to purchase private high-quality datasets and get the relevant licenses to access them. Secondtalent’s research suggests that the global data annotation market for LLMs alone is projected to grow from $2.32 billion in 2025 to nearly $10 billion by 2030. For large frontier models, data-related costs can exceed compute costs by up to 28 times.

Storing and preparing massive datasets still costs money. You would typically need terabytes or even petabytes of data to effectively train your model and you will need to store that data in cloud systems like AWS, Microsoft Azure or Google Cloud Platform. All these cost money, you still have to clean, filter, label, and format the data before feeding it to your model. All while complying with data privacy regulations like the GDPR and CCPA.

4. Human Labor and RLHF

Another major portion of LLM training cost comes from human annotation. You need to train your model with reinforcement learning from human feedback (RLHF) to truly improve the model’s behaviour and align the results it gives you to the type that will resonate well with users. Daniel Kang’s research suggests that this type of data annotation can cost you around $100 per high-quality annotation. Experts can charge you $40 or more per hour, especially for technical or specialized model domains.

Aligning the model to your goals after pre-training at scale requires even larger teams. Fine-tuning frontier models like Meta’s Llama 3.1 reportedly involved around 200 people and cost more than $50 million. That means you can be paying salaries that sum up to $40 million to $60 million per year in labor costs alone.

Training an LLM on Your Own Data

There are situations where you might want a model that understands internal company documents, or you want a model that performs specific tasks within specific sectors of your company, or you simply want better control of how the model handles data privacy. In such situations, you can take existing LLM training data and adapt the model to perform the function you want by feeding it custom data like product docs, API docs, stakeholder meeting transcripts, knowledge bases, support tickets, or structured records that reflect how the organization actually operates. This will allow the model to learn the company’s patterns, terminology, and context that you cannot find in public datasets. 

The quality of that custom data matters more than the volume of it. You need to decide the exact scope of data you want to infuse in the LLM. You then have to prepare and process the data because the model cannot process raw documents as they are.

Here are some data processing techniques that will come in handy when training an LLM on custom data.

Processing HTML Data

If you are feeding the model data obtained from HTML sources, you have to strip out navigation elements, mark-up tags, inline CSS styling, inline JavaScript, and ads. You also need to convert structured objects like lists, tables, and sample code blocks to markdown. The goal is to extract only the meaningful text that represents the actual content you need. If you do not process the HTML content, your model will give you unnecessary HTML layout formats instead of actual language patterns.

Processing PDF Data

Most PDFs store text in fragmented blocks that can break sentence structure and context e.g. text columns, images, tables, and figures. Unlike HTML, typical LLM models cannot recognize these structures, so it’s slightly more difficult to parse PDF documents directly into your model. If you have the option of avoiding PDF files entirely that would be better, but if you must use data from a PDF, then you can use libraries like pdfplumber, pypdf, and pdfminer to help you extract the information your model needs from PDFs.

Processing Office Documents

You might also need to process office documents like Word files, spreadsheets, or presentations before you feed them to the model. These documents usually include DOCX, PPTX, and XLSX files, so you have to rely on tools that can extract clean text from each format. For example, python-docx works well for DOCX files, openpyxl or pandas can handle XLSX files, and python-pptx will help extract text from PPTX files.

Deduplication

Deduplication is another critical step in preparing custom data for LLM training because internal company docs usually have repeated content across documents, versions, or repositories. The same text can appear in internal documents, email threads, chat logs, and support tickets.

If you provide this data to the model without removing the duplicates, the model can overemphasize certain phrases or concepts or even reference outdated versions of your product or service. This happens more often than people realize.

Research from CCNet shows that it is easy to see duplicated examples in large language datasets, and you will see the same issues when organizations build their own LLM training data. You might see a single paragraph in different places, or repeat many times inside one long email or conversation. If you train a model on this type of repeated data, the model will start to give too much weight to whatever it sees most often. It will tend to over-represent certain phrases, ideas, and explanations even if they are outdated or no longer accurate. This can bias the model and reduce the quality of its responses.

How to Use Synthetic Training Data for an LLM

Today, many teams rely on synthetic data to train their LLMs instead of depending only on the datasets they originally collected. Rather than waiting to gather more real-world data, they generate artificial examples that resemble real data and use those examples to expand their training sets. This approach allows them to scale faster, reduce annotation costs, and avoid some of the legal and licensing issues that come with scraping or purchasing proprietary datasets.

Synthetic data is data generated by other models that resembles real-world data text. The public datasets can be saturated and the private ones are expensive to access so people usually go for the option of generating data that mimics the context and structure of the data that you will get from a normal dataset. 

Synthetic data is not something you blindly plug into your pipeline. You must clean it, filter it, remove duplicates, and validate its quality before using it for training. Here are the steps you should take to prepare your data if you plan on using synthetic data to train your model. 

Step 1: Document Chunking and Splitting

The first step is to chunk your documents, meaning you have to break down the documents into smaller, more meaningful pieces. Each chunk has to retain enough context to be on their own but they cannot be too long. There are different ways to chunk a document, here is a simple Python representation of how you can chunk your documents

You can divide your documents into chunks of equal sizes or you can divide them based on context. Here is a simple Python representation of chunking your document:

pip install langchain langchain_openai

pip install langchain langchain_openai

from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import TokenTextSplitter

text_splitter = TokenTextSplitter(
   chunk_size=1024,  
   chunk_overlap=0 
)

loader = PyPDFLoader("chatbot_information.pdf")
chunks = loader.load_and_split(text_splitter)
print(f"Total chunks created: {len(chunks)}")

from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import TokenTextSplitter

text_splitter = TokenTextSplitter(
   chunk_size=1024,  
   chunk_overlap=0 
)

loader = PyPDFLoader("chatbot_information.pdf")
chunks = loader.load_and_split(text_splitter)
print(f"Total chunks created: {len(chunks)}")

This code above loads a PDF file and breaks it into smaller text chunks so your model can process it more easily. We are using LangChain to read the document and split the text into pieces of 1,024 tokens each. It then prints how many chunks were created from the file.

Step 2: Generating Vector Embeddings

Once you have successfully divided your docs, you need to convert them into embeddings so that the model can get the semantic meaning of each chunk. Embeddings are numbers that represent your text chunks and show the model what those chunks mean in its own language. This is what your document will look like to your model when converted to embeddings. 

You can pass each chunk through an embedding model like text-embedding-3-large, openAIEmbeddings, or a sentence-transformer model. These models can handle text up to a specific token limit, that is why you have to divide the initial doc first. It is easier to embed smaller chunks of text within the token limit of the model you are using.

Here’s a simple Python representation of converting chunks to embeddings:

from langchain_openai import OpenAIEmbeddings

embedding_model = OpenAIEmbeddings(
   api_key="YOUR_API_KEY"
)

raw_chunks = [
   "LLMs learn patterns from large amounts of text.",
   "Training data quality affects how models behave.",
   "Chunk chunk chunk chunk chunk."
]

content = raw_chunks
embeddings = embedding_model.embed_documents(content)
print(embeddings[0])

from langchain_openai import OpenAIEmbeddings

embedding_model = OpenAIEmbeddings(
   api_key="YOUR_API_KEY"
)

raw_chunks = [
   "LLMs learn patterns from large amounts of text.",
   "Training data quality affects how models behave.",
   "Chunk chunk chunk chunk chunk."
]

content = raw_chunks
embeddings = embedding_model.embed_documents(content)
print(embeddings[0])

The code above converts the data chunks into numerical representations called embeddings. These embeddings allow a model to understand the meaning of the text and work with it. 

Step 3: Establishing Semantic Context

After chunking your documents and converting them into embeddings, the next step is context generation. You are going to group related chunks together so the model can see connected ideas instead of isolated pieces of text. You start by using one chunk to act as a reference point. Think of this chunk as the anchor. The goal is to find other chunks that talk about the same topic or related ideas.

Once you have that reference chunk, you compare it against the rest of your data. You will group information that share the same context and ignore the ones that do not relate at all. Now you have a small collection of text that shares a common context. LLM models usually perform better when they receive information that is connected.

Here’s a simple Python representation of this:

import random
import numpy as np

reference_index = random.randint(0, len(embeddings) - 1)
reference_embedding = embeddings[reference_index]

contexts = [content[reference_index]]

# Define how similar chunks need to be
similarity_threshold = 0.8

similar_indices = []

for i, embedding in enumerate(embeddings):
   # Compute cosine similarity
   dot_product = np.dot(reference_embedding, embedding)
   norm = np.linalg.norm(reference_embedding) * np.linalg.norm(embedding)
   similarity = dot_product / norm

   if similarity >= similarity_threshold:
       similar_indices.append(i)

for i in similar_indices:
   contexts.append(content[i])

import random
import numpy as np

reference_index = random.randint(0, len(embeddings) - 1)
reference_embedding = embeddings[reference_index]

contexts = [content[reference_index]]

# Define how similar chunks need to be
similarity_threshold = 0.8

similar_indices = []

for i, embedding in enumerate(embeddings):
   # Compute cosine similarity
   dot_product = np.dot(reference_embedding, embedding)
   norm = np.linalg.norm(reference_embedding) * np.linalg.norm(embedding)
   similarity = dot_product / norm

   if similarity >= similarity_threshold:
       similar_indices.append(i)

for i in similar_indices:
   contexts.append(content[i])

This code above randomly picks one piece of text and finds other pieces that are very similar to it using cosine similarity. It compares their embeddings (numerical representations) and checks whether they meet a similarity threshold of 0.8. If they are similar enough, it groups them together into the same context list.

Step 4: Automated Query Generation

This is the stage where you use a capable LLM like ChatGPT to generate queries from the context you created. The idea is to turn each document chunk into realistic tasks or questions that a potential user of your model might actually ask. 

You first have to provide your model with a prompt template that instructs it to generate a list of JSON objects. Each object should contain an input key that will act as a query. This query can now be a user’s statement or a question that your model can answer with the context you gave it. 

Here’s how you can generate queries in Python:

from langchain_openai import ChatOpenAI

# Example chunks of text you already created earlier
contexts = [
   "LLMs are trained on large text datasets to predict the next token.",
   "Fine-tuning allows a model to adapt to specific tasks or domains."
]

# Prompt template
prompt = f"""
Act as a copywriter.

Based on the following context, generate a list of JSON objects.
Each object should have an `input` key.
The value of `input` should be a question or statement that a user
might realistically ask based on the context.

Context:
{contexts}
"""

# Call the model
llm = ChatOpenAI(openai_api_key="YOUR_API_KEY")
response = llm.invoke(prompt)

# Print the generated queries
print(response)

from langchain_openai import ChatOpenAI

# Example chunks of text you already created earlier
contexts = [
   "LLMs are trained on large text datasets to predict the next token.",
   "Fine-tuning allows a model to adapt to specific tasks or domains."
]

# Prompt template
prompt = f"""
Act as a copywriter.

Based on the following context, generate a list of JSON objects.
Each object should have an `input` key.
The value of `input` should be a question or statement that a user
might realistically ask based on the context.

Context:
{contexts}
"""

# Call the model
llm = ChatOpenAI(openai_api_key="YOUR_API_KEY")
response = llm.invoke(prompt)

# Print the generated queries
print(response)

This code above will use your model to automatically generate questions based on the text you provide. It takes that context and sends it to the model with clear instructions, and asks it to return realistic questions that humans can ask in JSON format.

Step 5: Query Evolution and Variation

You can take the queries you created and try to get new variations of them instead of just writing new questions from scratch all the time. This can help you test different ways your model might think or respond to different versions of the same question. 

You can do this by setting up a couple of templates that will help your model better understand and translate certain contexts. You can set as many templates as you want.

You can set up a multi-context understanding template, for scenarios where the query references more than one piece of information.

context = """
The API only allows GET requests.
The client sent a POST request.
The server blocks unsupported methods.
"""

original_input = "Why did the server return an error?"

multi_context_template = """
Rewrite the input so it requires using all parts of the context.


Context:
{context}


Input:
{original_input}


Rewritten Input:
"""

context = """
The API only allows GET requests.
The client sent a POST request.
The server blocks unsupported methods.
"""

original_input = "Why did the server return an error?"

multi_context_template = """
Rewrite the input so it requires using all parts of the context.


Context:
{context}


Input:
{original_input}


Rewritten Input:
"""

The code above sets up a small example where you give a model some context and a basic question. The template then tells the model to rewrite that question so the answer must use all parts of the context. It forces the model to create a smarter, more detailed version of the original question.

You can also set up a multi-step reasoning template, where the model needs to think through several steps before it gives you an answer.

context = """
The API only allows GET requests.
The client sent a POST request.
The server blocks unsupported methods.
"""

original_input = "Why did the server return an error?"

reasoning_template = """
Rewrite the input so it requires step-by-step reasoning.


Context:
{context}


Input:
{original_input}


Rewritten Input:
"""

context = """
The API only allows GET requests.
The client sent a POST request.
The server blocks unsupported methods.
"""

original_input = "Why did the server return an error?"

reasoning_template = """
Rewrite the input so it requires step-by-step reasoning.


Context:
{context}


Input:
{original_input}


Rewritten Input:
"""

The code takes a simple question and rewrites it so the model has to think through the details before answering. It uses the context you provided and turns the original question into one that requires step-by-step thinking.

Lastly, you can set up a hypothetical scenarios template, where the queries asks what would happen in a “what if” situation.

context = """
The API only allows GET requests.
The client sent a POST request.
The server blocks unsupported methods.
"""

original_input = "Why did the server return an error?"

hypothetical_template = """
Rewrite the input as a "what if" question.

Context:
{context}

Input:
{original_input}

Rewritten Input:
"""

context = """
The API only allows GET requests.
The client sent a POST request.
The server blocks unsupported methods.
"""

original_input = "Why did the server return an error?"

hypothetical_template = """
Rewrite the input as a "what if" question.

Context:
{context}

Input:
{original_input}

Rewritten Input:
"""

The code above takes a chunk of information and a question, then turns that question into a “what if” version. It changes the original question into a hypothetical scenario. This helps you create different versions of the same question, which makes your training data more varied and useful.

Now all you need to do is repeat steps 1–5 until you have the right amount and quality of synthetic dataset that you feel your model needs to perform optimally.

Dos and Don’ts of using LLM Training Data

Here are some practical guidelines you must follow when dealing with LLM training data:

Conclusion

LLM training data shapes how a model reasons, responds, and behaves, so the decisions you make around them will determine how efficient your LLM will be at the end. Your model will not perform and scale properly if you try to increase the model’s size parameters without scaling it with the right amount of data tokens that model needs. Obtaining and preparing this data can cost serious amounts of money so you always have to keep the data budget in mind and aim for data that reflects how humans write, ask questions, and solve problems. You can also use synthetic data from another model to expand your dataset, but you must refine that data, fine-tune it and validate all of it.  You must filter out low-quality outputs and remove duplicates to make sure that the generated samples align with your training objectives.

Key takeaways:

• The data you train your model with will shape how it thinks, answers questions, and solves problems.
• Prioritize the quality of data you feed the LLM, it is not just about the volume or size. 
• Training an LLM from scratch can be expensive, so refining and fine-tuning existing data can save you some cost.
• You can provide custom data to your model if you want the model to reflect your company’s domain or use case.
• You can generate synthetic data to help you scale your training data without the cost of collecting more real-world data, but you need to carefully process and fine-tune the data before you use them on your model.

In this article, we covered what LLM training data looks like in practice, where it comes from, how much it can cost, and what it means to train an LLM on your own data. We also broke down the real costs behind GPUs, LLM infrastructure, obtaining and processing data, as well as RLHF. Lastly, we explored how you can use synthetic data to scale the training data you’re feeding your model and save you some cost obtaining that data from private datasets.

Successful LLM training is not about collecting the most data, it is about choosing the right data, preparing it carefully, and balancing it against your budget. That balance is what determines whether your model will perform optimally in a way that can actually help humans.

Frequently Asked Questions