I found the most easiest explanation to LLMs, this might surprise you

1. 1Read the article to understand the basics of LLMs and their prediction process
2. 2Tokenize a prompt using a tool like Hugging Face's Tokenizers to see how words are broken down into tokens
3. 3Explore the concept of embeddings and how they represent words in a vector space
4. 4Apply the prediction loop to a simple example to understand how LLMs generate text
5. 5Research the training process of LLMs to learn how they become good at predicting words

This explanation benefits anyone on a team working with LLMs, especially those in AI engineering, research, or development, as it provides a foundational understanding of how LLMs learn and predict words.

💡 LLMs predict words through a process of tokenization, embeddings, attention, and a prediction loop, rather than through traditional thinking or learning

🤖 Did you know LLMs like ChatGPT don't think like we do? Learn how they predict words and understand their learning process! #LLMs #AI #ChatGPT