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 an assembly line 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 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 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 cook dinner while doing taxes. 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 results when you ask it to do too many things simultaneously?

Here's a simple linear chain example:

• First prompt: "List out top 5 laptop brands."
• Second prompt: "For each brand from this list: [paste list], identify their main features and strengths."
• Third prompt: "Based on these features: [paste features], recommend which laptop to buy for my needs: college student, gaming, under $1000."

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Which step(s) in this chain would benefit from using deep research techniques?

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

Always include relevant output from earlier prompts when it's needed for context.