AI Fundamentals 101

Artificial Intelligence isn't magic—it's pattern recognition at scale. Let's demystify how modern AI actually works.

The Foundation

What is an LLM? A Large Language Model (like GPT-5, Claude Opus, or Gemini 3) is a neural network trained on billions of text examples. It learns patterns: "When I see these words, this word usually comes next."

Think of it like ultra-advanced autocomplete. Your phone predicts "How are you?" after "Hi, "—LLMs do this at a massive scale with complex context.

How LLMs Are Trained

Training an LLM involves three key steps:

Data Collection: Gather massive amounts of text from books, websites, code repositories, and more (often 1+ trillion words).
Pattern Learning: The model processes this data millions of times, adjusting billions of internal parameters to predict "what comes next."
Fine-Tuning: Human trainers guide the model to be helpful, truthful, and safe through feedback (RLHF - Reinforcement Learning from Human Feedback).

Neural Network Architecture

Billions of parameters (connections) trained to recognize patterns in language. Each "neuron" learns to detect specific features: grammar, sentiment, facts, and more.

What LLMs Can and Can't Do

✅ What They're Great At

• Writing, summarizing, and translating text
• Answering questions from their training data
• Generating code and explanations
• Recognizing patterns and sentiment
• Creative tasks (stories, poems, ideas)

❌ What They Struggle With

• Accessing real-time information
• Performing calculations reliably
• Knowing when they don't know something
• Understanding true causality
• Maintaining perfect consistency

The key insight: LLMs don't "think" or "understand" like humans. They generate text that looks intelligent because they've seen similar patterns billions of times. This is why they can write a perfect essay but also confidently state a false fact.

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