How LLMs Work: A Deep Dive into Tokenization, Attention & Prediction

Large Language Models (LLMs) are transforming how machines understand and generate human language. In this blog, we explore exactly how LLMs work — from tokenization to deep neural networks — and why it matters for building powerful AI.

🔹 Tokenization & Embeddings

LLMs start by breaking input text into tokens — smaller chunks like words or sub-words. These tokens are then converted into vectors in a high-dimensional space. Words with similar meanings cluster together, forming the basis of semantic understanding.

🔹 The Attention Mechanism (Self-Attention)

The attention mechanism allows the model to focus on relationships between words in context. For example, “bank” in “river bank” isn’t confused with a financial institution. This is done via self-attention blocks that dynamically weigh the importance of each token relative to others.

🔹 Feed-Forward Layers

Post attention, tokens pass through multiple feed-forward neural layers that refine their contextual meaning. Each layer improves understanding by learning deeper relationships and abstract features of the input.

🔹 Deep Learning Iteration

LLMs use dozens to hundreds of layers in succession. This iterative structure, filled with matrix multiplications and optimizations, defines the “deep” in deep learning and is key to capturing complexity in language.

🔹 Prediction & Sampling

At the final stage, the model uses its internal vector representation to generate the next word based on probability. This sampling process continues, creating fluent and context-aware text output.

✅ Why This Matters

These core mechanisms are what power advanced LLMs like ChatGPT, Claude, or Gemini. Understanding how LLMs work is crucial for developers and teams building scalable and responsible AI tools.

At BeeNeural, we equip organizations and individuals with the tools and knowledge to harness this AI power effectively.

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