About this module
This module keeps the topic grounded in normal work. Learners start with advanced prompting strategies for complex work, then practice using prompt chains, critique passes, alternative viewpoints, rubrics, and staged outputs. The risk is making a prompt system so complex that no one can maintain it, so the module keeps review and judgment close to the work. By the end, learners can use advanced patterns only when the task justifies them and know what to check before moving an AI-assisted result forward.
Key takeaways
You've learned the fundamentals. Now it's time to combine them into techniques that handle real, complicated work. You've mastered the basics. Now let's look at three techniques serious A.I. users lean on when the stakes, and the complexity, go up.
The first move is combining three levers at once: assign a role, ask the model to reason through the problem step by step, and specify exactly how the answer should be formatted. Act as a pricing strategist, think through the trade-offs step by step, then give me only a three row table, does three jobs in one prompt, and the output shows it.
Here's the mindset shift behind all of this. A great prompt reads less like a command, and more like a well-briefed colleague who already knows the constraints. The second strategy is prompt chaining. Instead of one giant ask, you run a sequence, draft, then critique, then refine, each prompt building on the last output.
A typical chain runs three prompts. First, draft an outline. Second, ask the model to critique its own gaps. Third, rewrite using that critique. Each step feeds the next. This second prompt only makes sense because it points back at what the model just produced. That's the core of chaining, each prompt depends on the one before it.
The third strategy is self-critique. Before you accept an answer, ask the model to check its own work for errors, gaps, or claims it can't actually support. Review your last answer, flag any unsupported claims or logic gaps, then rewrite it, catches more mistakes than most people expect. It costs one extra message.
The difference between these two outcomes is almost never a smarter model. It's one extra prompt asking the model to check its own work before you use it. To recap: stack role, reasoning, and format together, chain complex tasks across multiple prompts, and always ask the model to check its own work before you trust it.
You now have three strategies that separate advanced prompting from basic prompting. Next, we'll turn your best prompts into a personal library you can reuse anytime.
