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 development pipeline 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 coding 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 development 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 write code while reviewing architecture. 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 code results when you ask it to do too many things simultaneously?

Here's a simple linear chain example:

• First prompt: "List the top 5 Python testing frameworks."
• Second prompt: "For each framework from this list: [paste list], identify their main strengths and weaknesses."
• Third prompt: "Based on these comparisons: [paste analysis], recommend which testing framework would work best for a REST API project."

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Do you remember Deep Research tool? 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.