How LLMs Work: Tokens, Embeddings, and Transformers

📰 Medium · LLM

Learn the fundamentals of LLMs, including tokens, embeddings, and transformers, to understand how they process and generate human-like language

beginner Published 23 Apr 2026

Action Steps

Read the article series to gain a comprehensive understanding of LLM engineering
Explore the concepts of tokenization and embeddings in LLMs
Apply transformer architectures to improve language model performance
Experiment with different LLM models and evaluate their results
Use tools like Hugging Face's Transformers library to implement LLMs in practice

Who Needs to Know This

NLP engineers, data scientists, and AI researchers can benefit from this article to improve their understanding of LLMs and develop more effective language models

Key Insight

💡 LLMs rely on tokenization, embeddings, and transformer architectures to process and generate human-like language