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.
Engagement Message
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.
Engagement Message
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.
Engagement Message
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 CRM companies."
- Second prompt: "For each company from this list: [paste list], identify their main pricing strategy."
- Third prompt: "Based on these pricing strategies: [paste strategies], recommend how to price our CRM product."
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.
Always include relevant output from earlier prompts when it's needed for context.
