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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
Referenced
• A complete guide on RLHF
• AGI vs ASI
• Andrej Karpathy on LLMs
• Andrej Karpathy on vibe coding
• Anthropic’s guide on building effective agents
• Anthropic’s guide to reducing hallucinations
• Fine-tuning vs RAG vs prompt engineering
• Guide to model context protocol (MCP)
• How LLMs work
• How fine-tuning works
• How top models tokenize words
• How training and pre-training works
• Ilya Sutskever on AGI
• Ilya Sutskever on next-word prediction
• Lenny’s Podcast on prompt engineering
• Make product management fun again with AI agents
• RLHF explainer
• Sam Altman on synthetic data
• Technical deep dive on transformers
• What are transformers?
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About
Welcome to Lenny’s Reads, where every week you’ll find a fresh audio version of my newsletter about building product, driving growth, and accelerating your career, read to you by the soothing voice of Lennybot.
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