Prompt Engineering for Claude Models: A Practitioner's Guide

This course contains the use of artificial intelligence general practice tests.

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.

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.

• One emphatic tool instruction, written two model generations ago, deleted: 38% fewer tool calls, no change in task success.
• One clause of reasoning added to a bank-details rule: a fraud control went from holding 12 of 41 probes to 39 of 41 — and became less likely to over-refuse.
• Eleven format prohibitions replaced by one sentence describing the target.
• A six-line bug fix that came back as 430 lines across 11 files, and the five specific clauses that cut diff sizes 70%.
• An overnight agent that asked permission at 19:11 and waited 13 hours for an answer.

Prompting advice ages badly, and most of it is published without measurement. This course is built the other way round: you build an evaluation set in Section 1, and every technique afterwards is something you can prove or disprove on your own system. Section 6 then teaches the thing that survives the next release — how to read a model behaviour guide, how to run an effort sweep, and the eight-audit migration checklist that took one team from three weeks to two days.

Zero bullet-point lectures. Every lecture is a motion-driven scene — animated architecture diagrams, live prompt-versus-response demos, terminal walkthroughs of real eval runs, and before/after comparisons with the metrics moving on screen.

Engineers, ML engineers and technical PMs who already call an LLM API and are tired of their prompt file becoming four hundred lines nobody dares touch. No ML background needed.

This is an independent, unofficial course. It is not affiliated with, endorsed by, or sponsored by Anthropic. Claude is a trademark of Anthropic, PBC. Always check the official documentation for current model behavior.