Course overview

Lesson objective

By the end of this overview, you will know what you are going to produce (a "delegate / verify / keep" grid applied to ten tasks from your job), which running case the course follows and which four modules take you there.

Where it fits

Overview

What will I be able to do at the end of this course, and in what order?

Lessons in this module

1. Course overview (this lesson)

What you will learn in the course

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.
• Locate what the model knows (training data, knowledge cutoff, uneven coverage) and what web search, provided documents and connectors change.
• Explain the context window as working memory (limit, degradation over long contexts, no memory across conversations without a dedicated feature) and derive working rules from it.
• Assess what reasoning (thinking) and reading images or documents bring, and what they do not guarantee.
• Sort the tasks of your job into "delegate / verify / keep" based on the cost of an error, how easy it is to verify, the model property involved and the value of doing it yourself.

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