I wrote a beginner-friendly explanation of how Large Language Models work

Understanding Large Language Models: A Beginner-Friendly Breakdown

As AI continues to reshape the digital landscape, many enthusiasts and newcomers alike seek to grasp how Large Language Models (LLMs) operate behind the scenes. To aid in this learning journey, I have authored my first technical blog aimed at providing a clear, accessible explanation of the core concepts underpinning LLMs.

The central aim was to develop a straightforward mental model of the complete text generation process. This involves several interconnected stages, each playing a vital role in how LLMs interpret and generate language. I focused on presenting these stages at a high level, emphasizing how they interrelate without delving into complex implementation details.

The Generation Loop Explained

1. Tokenization
   The process begins with breaking down raw text into manageable units called tokens. These can be words, subwords, or characters, serving as the foundational building blocks for the model.

2. Embeddings
   Tokens are then transformed into numerical vectors — known as embeddings — which capture the semantic nuances and contextual information necessary for meaningful understanding.

3. Attention Mechanism
   The attention component enables the model to weigh the relevance of different tokens relative to one another, effectively focusing on the most pertinent parts of the input data during processing.

4. Probabilities
   Based on the contextual information, the model predicts the likelihood of potential next tokens, assigning probabilities that guide its subsequent choices.

5. Sampling
   Finally, from these probability distributions, the model samples the next token, gradually building coherent and contextually appropriate text.

My goal was to make this complex process accessible and intuitive, serving as a starting point for those new to the field or looking to deepen their understanding of how LLMs function internally.

You can read the full article here: https://blog.lokes.dev/how-large-language-models-work

I welcome any feedback, particularly from professionals working with LLMs or learners exploring Generative AI. Your insights can help refine the explanation and support others in demystifying these powerful models.

Feel free to share your thoughts and questions—I look forward to engaging discussions that promote clarity and understanding in this rapidly evolving domain.