How LLMs Actually Generate Text (Every Dev Should Know This)
How do ChatGPT, Claude, and other LLMs actually generate text? This video breaks down the exact 5-step process that runs every time you hit send — from tokenization to the final word.
Most developers use LLMs daily without understanding what's happening under the hood. But knowing the mechanism changes how you prompt, debug, and build with these models.
In this video, you'll learn:
→ Why LLMs read tokens, not words (and why that matters for your API costs)
→ How embeddings create a "meaning space" where similar concepts cluster together
→ What attention actually does — with real examples you can visualize
→ Why the model genuinely doesn't know what it will say next
→ How temperature and top-p sampling shape your outputs
Timestamps:-
0:00 - The surprising truth about LLM generation
0:33 - The 5-step pipeline overview
0:58 - Step 1: Tokenization
2:04 - Step 2: Embeddings and meaning space
3:17 - Step 3: The Transformer and Attention
4:46 - Step 4: Probability distributions
5:46 - Step 5: Sampling, temperature, and top-p
07:05 - The autoregressive loop
08:05 - 3 practical insights you can use today
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