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

Think of it like an assembly line for AI tasks. Each step focuses on one specific piece of the puzzle.

Engagement Message

Have you ever felt overwhelmed trying to get AI to handle a complex product strategy or user research request all at once?

Here's how prompt chaining works: You break a big product management 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.

Engagement Message

What's a complex product 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 analyze user feedback while creating a roadmap. Especially smaller/faster models.

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

Engagement Message

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

Here's a simple linear chain example:

• First prompt: "List the top 5 user pain points from this feedback data."
• Second prompt: "For each pain point from this list: [paste list], identify potential product solutions."
• Third prompt: "Based on these solutions: [paste solutions], prioritize them using impact vs effort framework."

Do you remember Deep Research tool?

Engagement Message

Which step(s) from this chain should we use it for?

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