You’ve interacted with LLMs thousands of times, but do you really understand how they work?

The answer might surprise you—and it’s both simpler and more fascinating than you think.

Here’s the breakdown:

1️⃣ Tokenization & Vector:
Embedding. Every word you type gets converted into numbers (tokens), then multi-dimensional vectors. This is how the model “understands” context. Your question “How do I write Apex code?” becomes mathematical coordinates capturing meaning across hundreds of dimensions. These embeddings are precise enough that synonyms end up near each other in semantic space.

2️⃣ The Transformer Architecture & Attention Mechanism:
This is where the magic happens. Transformers use “attention”—imagine a spotlight identifying which words matter most for the answer. When you ask about Salesforce Apex versus Lightning Web Components, the model focuses on critical terms while understanding relationships. Multiple attention heads work simultaneously, each capturing different patterns. Some track technical concepts, others track intent, others track relationships. This parallel multi-head attention enables sophisticated reasoning.

3️⃣ Matrix Multiplication & Neural Weights Under the hood:
billions of mathematical operations per inference. Every tokenization, attention calculation, and relationship analysis happens through optimized matrix multiplication. Those “weights” (parameters) are learned during training. A 7B parameter model means 7 billion individual values multiplied and summed. GPT-3 has 175 billion. These aren’t random—they’re precisely tuned through gradient descent over weeks of compute.

4️⃣ Autoregressive Prediction & Probability Distribution:
Here’s the critical insight: LLMs don’t think—they predict. Given your input, they calculate a probability distribution over all possible next tokens (words). They pick the most likely one based on learned patterns, then repeat recursively. It’s sophisticated probabilistic inference, not deterministic reasoning. Temperature and top-p settings tune how confident or creative the model becomes.

The Real Insight:
LLMs are sophisticated pattern-matching engines trained on trillions of tokens. They’re not conscious. They’re not reasoning in the human sense. They’re executing mathematical operations so extraordinarily complex and at such massive scale that emergent behavior appears—mimicking understanding, logic, and creativity. That’s simultaneously humbling and absolutely fascinating. Patterns learned during training generalize to novel problems. It’s why they sometimes hallucinate and sometimes astound us.

Understanding this fundamentally changes how you build with AI. It’s not magic. It’s mathematics that became powerful enough to feel like intelligence.

#LLMs #MachineLearning #AI #AI&MachineLearning #Salesforce #DataScience #deepLearning

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