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You’ve probably heard terms like LLM, transformer, and hallucination, but do you really know what they mean?
In this episode, I walk through 20 of the most common AI terms with dead-simple explanations you can actually understand (and use).
In this episode, you’ll learn
What a “model” actually is
The difference between pre-training, fine-tuning, and RLHF
What transformers are—and why they changed everything
How prompt engineering and RAG improve model outputs
What AGI and ASI really mean
The difference between LLMs, GenAI, and GPT
Why models hallucinate (and how to prevent it)
What synthetic data is—and why it matters
How vibe coding works and what agents can actually do
What MCP, inference, and tokens are in plain English
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