You've learned iterative prompting. Now let's explore prompt chaining - breaking complex tasks into a sequence of connected prompts where each output becomes the next input.

Think of it like a project management workflow for AI tasks. Each step focuses on one specific piece of the puzzle.

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Have you ever felt overwhelmed trying to get AI to handle a complex, multi-step project management request all at once?

Here's how prompt chaining works: You break a big task into smaller, logical steps. The AI completes step one, you take that output, and feed it into step two.

Each prompt is focused and clear. No confusion, no overwhelming complexity - just one thing at a time.

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What's a complex project management task you do regularly that involves multiple steps?

Why does chaining work better than one massive prompt? AI gets confused with too many instructions at once, like asking someone to create a project plan while analyzing risks. Especially smaller/faster models.

Chaining gives AI focus. Each step has a clear goal, making responses more accurate and reliable.

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Have you noticed AI giving worse project management results when you ask it to do too many things simultaneously?

Here's a simple linear chain example:

• First prompt: "List the top 5 project management methodologies for software teams."
• Second prompt: "For each methodology from this list: [paste list], identify their main strengths and weaknesses."
• Third prompt: "Based on these comparisons: [paste analysis], recommend which methodology would work best for a remote team building a mobile app."

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Does this make sense?

Common mistake: Making each step too complex. Keep individual prompts simple and focused. Another mistake: Not providing enough context from previous steps.