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About this course
This course turns prompt engineering from trial-and-error into something you can reason about. You'll work through multiple-choice questions on instruction design, few-shot examples, chain-of-thought, output formatting, context management, tool use, and the failure modes that make prompts break in production. It's built for developers building on LLM APIs, analysts and writers who use AI daily, and anyone preparing for an AI-focused interview.
Why practice prompt engineering
Most people learn prompting by pasting something in and hoping. That works until the output has to be reliable across a thousand inputs. Answering questions forces you to commit to a choice — which technique, what order, how much context — and the explanation tells you immediately whether your reasoning was sound or just lucky.
How this course works
- Short MCQ sessions you can finish in a coffee break
- Every answer comes with an explanation of why it's right and why the alternatives fail
- Scenario questions that hand you a bad prompt and ask what to fix first
- Difficulty ramps from vocabulary to real design and debugging calls
What you'll walk away with
A repeatable method for structuring prompts, a clear sense of when few-shot beats instructions and when it doesn't, and the habit of diagnosing a bad output instead of rewriting blindly.