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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.

To hear more, visit www.lennysnewsletter.com