Chain of Thought Prompting: Making AI Think Out Loud

Ever wondered what's going on inside an AI's "head" when it answers your questions? That's exactly what Chain of Thought Prompting helps us figure out. I've been experimenting with this fascinating technique, especially with local models like Qdub (Quinn with Questions), and I want to share what I've learned about getting AI to show its work.

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What's This All About?

Think back to your math classes where teachers always said "show your work." Chain of Thought Prompting is basically that, but for AI. Instead of just getting a final answer, we get to see how the AI reached its conclusion. Pretty cool, right?

Why Should You Care?

Let's be honest - trusting AI can be tricky. When a model just spits out an answer, it's hard to know if it's really thought things through. That's where Chain of Thought Prompting comes in. By seeing the AI's reasoning process, we can better judge if its answers make sense.

The Real-World Challenges

Working with reasoning models isn't all sunshine and rainbows. Here's what I've run into:

First off, these models can be pretty slow. It's like waiting for someone to write out their entire thought process instead of just giving you the answer. And sometimes, they can get a bit chatty - imagine asking for directions and getting the entire history of road construction in your area!

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A Smart Solution: Prompt Chaining

Here's a neat trick I've found that really works: instead of using just one model, use two in sequence. I call it Prompt Chaining (okay, others call it that too). Here's how I do it:

1. First, I use Qdub to think through the problem thoroughly

2. Then, I pass that output to a model like Quinn 2.5 to clean it up and extract just the important bits

Real Example: Creating SEO Titles

Let me show you how this works in practice. Say you're trying to come up with blog titles. Instead of just asking for titles directly, you can:

1. Have Qdub brainstorm and think through what makes a good title

2. Then let Quinn 2.5 polish those ideas into actual, usable titles

The result? Much better titles than you'd get from either model alone.

Setting Up Your Own System

If you want to try this yourself, here's what I recommend:

Start simple - pick one specific task you want to improve. Maybe it's writing code, maybe it's content creation. Then design your prompts to work together, kind of like passing notes between two experts who each have their own specialty.

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Quick Tips from My Experience

• Be super clear with your instructions

• Set some boundaries (unless you enjoy reading novel-length responses)

• Don't be afraid to experiment with different models for different parts of your chain

Common Questions I Get

"Is this really worth the extra effort?" If you care about understanding how AI reaches its conclusions (and you probably should), then yes, absolutely.

"Can I run this on my own computer?" Yep! If you've got decent hardware (like a MacBook M2 Pro), you can run local models like Qdub just fine.

"What's this best used for?" I've found it especially helpful for tasks where reasoning matters - think content creation, data analysis, or coding challenges.

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Wrapping Up

Chain of Thought Prompting isn't just another AI buzzword - it's a practical tool that helps us build better, more trustworthy AI solutions. By combining it with Prompt Chaining, we can get the best of both worlds: thorough reasoning and clean, usable outputs.

Whether you're an AI enthusiast or a developer looking to improve your workflows, understanding these techniques is becoming increasingly important. And trust me, once you start seeing how AI "thinks," you'll never want to go back to black-box answers again.