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What is LLM, and how does LLM training actually work?

Large language models (LLMs) power today’s most advanced AI systems – from chatbots to coding assistants. But how are they trained? Where does their training data come from? And what does the full training pipeline look like?

In this video, you’ll learn what a large language model is, how LLM training works, and why high-quality training data is critical for performance. We’ll walk through the full process – from pre-training on massive datasets to fine-tuning for specific tasks. You’ll also discover the main LLM training data sources, how data size and composition affect results, and what makes modern AI training so computationally intensive.

We’ll cover key concepts like model training, AI training pipelines, pre-training vs. fine-tuning, and how to update LLM training data efficiently. Whether you're researching how LLMs work, exploring how to train LLM models, or building your own dataset, this video provides a clear, structured explanation of large language models and their data foundations.

📚 *OTHER RESOURCES*
✏️ Explore our in-depth article on what an LLM (Large Language Model) is: https://oxy.yt/hcZA 
✏️ Want a deeper dive? Read our guides on what AI model training is and how AI is trained step by step: https://oxy.yt/4cXl & https://oxy.yt/GcCp 
✏️ Learn more about LLM training data and the key public data sources used to train large language models: https://oxy.yt/TcVN 

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⏳ *TIMESTAMPS*
0:00 Intro
0:47 What is LLM (Large Language Model)?
1:47 How do LLMs work?
3:24 LLM use cases & real-world applications
4:52 LLM training data explained
5:31 Where do LLMs get their data?
6:28 Why public web data is crucial
7:07 Training data size specifics
7:57 Legal & ethical considerations
8:40 How to create your own training dataset
10:16 Tools & frameworks for training LLMs
11:00 Where to find training datasets
11:29 Cost considerations
12:01 Start small – train smaller models first
13:34 Outro

#LLM #TrainingData #LargeLanguageModels

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Oxylabs

A beginner-friendly overview of Large Language Models covering what they are, how transformer-based neural networks work, and the types of training data that power them. Covers the four main data pillars (public web, structured knowledge, technical content, human interaction), the data pipeline workflow (collect, clean, label, split), key tools like PyTorch, Hugging Face, and Apache Spark, and practical advice on fine-tuning vs. training from scratch. Ends with a pitch for Oxylabs' web scraping tools as a data collection solution for LLM projects.

What is LLM? Training Data Sources Explained (2026)