🧠 How Large Language Models Work – A Beginner-Friendly Deep Dive

Hey DEV community! 👋

I just published the first part of my article series on How Large Language Models (LLMs) Work, inspired by Andrej Karpathy’s legendary insights into AI systems.

🔗 Read it here on Medium: How Large Language Models Work: Part 1

In this post, I explain:

• What LLMs really are (and what they aren’t)
• Why they’re more like giant autocomplete engines than digital brains
• How neural networks and tokenization work under the hood
• The 3-stage training process: Pre-training, Fine-tuning, RLHF
• The rise of LRMs (Large Reasoning Models) and what Apple’s recent research says about their limitations

TL;DR: LLMs are amazing — but they don’t “think.” They just predict the next word, really well.

Whether you're just stepping into AI or you're curious how ChatGPT, Claude, or Gemini 2.0 actually work, this piece is written in plain language, with analogies and real examples.

Would love your thoughts — does understanding how LLMs work make them feel more or less impressive to you?

💬 Let's talk AI below — and feel free to drop any feedback or follow-up questions!