Prompt Engineering for Claude Models: A Practitioner's Guide

About this course

This course contains the use of artificial intelligence. Most prompting courses teach you to add things. This one will mostly teach you to take things out. The craft inverted. The older discipline was compensation: models under-delivered, so you pushed. You wrote instructions in capital letters to make a tool fire. You said always double-check your work, because otherwise it wouldn't. You forced a JSON shape by starting the reply yourself. Every one of those existed to drag a reluctant model up to a standard. Current models arrive at that standard on their own. They check their work, they reach for tools, they explore. So those same instructions now stack on top of behaviour that is already there — and you get an agent that verifies three times, spawns subagents for a task it could have finished itself, and writes nine paragraphs when you wanted two. In this course, four of the five largest measured effects come from deleting a line, not adding one. You will work at one company for the whole course Abstract prompting advice is close to useless, so there are no toy examples here. You join Vantel, a 400-person B2B invoicing company, on day one, and you stay there for all 34 lectures. Vantel runs three AI surfaces that between them cover essentially everything prompting has to handle: Ledger Assist (in-product chat), Invoice Intake (a document-extraction pipeline), and Cobalt (an internal coding agent that runs unattended for hours). You will meet the engineers, read their prompts, and watch their measurements. Every claim in this course has a number attached One emphatic tool instruction, written two model generations ago, deleted: 38% fewer tool calls, no change in task success.

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