Module 2 of 5Lesson 1 of 2~15 min
An LLM predicts how a text continues, and that explains almost everything
Most of the surprises an AI assistant springs on you come from the way it builds its answers. This lesson helps you understand that mechanism without needless jargon, then anticipate, for a task in your job, whether the main risk is a factual error or a mere flaw of form.
Lesson objective
By the end of this lesson, you will be able to explain how an LLM produces an answer (tokens, next-token prediction, pretraining then post-training) and to infer, for a given task, whether the main risk is an invention or a mere clumsiness of form.
Topics covered
- how LLMs work
- tokens
- pretraining
- RLHF
- generative AI explained
Where it fits
How an LLM produces an answer
Where does an AI answer come from, and why can it be wrong while sounding right?
Lessons in this module
- An LLM predicts how a text continues, and that explains almost everything (this lesson)
- Hallucinations, sycophancy, variability, the three traps to anticipate
What you will learn in this module
This lesson is part of the course Understand what LLMs do well, and where they fail
- Explain how an LLM produces an answer (tokens, next-token prediction, pretraining then post-training) and why a fluent answer is not a verified answer.
- Anticipate hallucinations, sycophancy, poorly calibrated confidence and run-to-run variability, and choose the right check for each case.
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