After teaching LLMs to ‘Think’, you teach them to ‘Reason’
Why 'Thinking step-by-step' isn't enough to get the best output possible.
You prompt ChatGPT with a tricky problem.
It walks you through a clear explanation, step by step. But somewhere along the way, something feels off. One flawed assumption, and the entire logic collapses.
You’re back to square one.
In my previous post, I explored how Chain-of-thought approach helps LLMs to ‘think’ better.
Yes, Chain-of-Thought helps the model think more clearly. But what happens when its single line of thinking fails?
That’s where Enhanced Chain-of-Thought comes in.
It doesn’t just teach the AI to reason.
It teaches it to reason flexibly.
And that makes all the difference.
🔁 The Problem With One-Track Thinking
Chain-of-Thought (CoT) prompting was already a game-changer.
By encouraging AI to “think step-by-step,” we saw huge gains in how well it handled logic, math, and complex tasks. Instead of jumping to conclusions, the model broke down problems like a human would… and that alone led to massive accuracy improvements.
But CoT has a hidden limitation.
When you only show the model one way to think, it learns to mimic, not to adapt.
That’s fine… until the problem changes shape. Or your original prompt doesn’t match the format it was trained to follow. Then the logic crumbles, and the model can’t recover.
Enter Enhanced Chain-of-Thought: a method designed to fix that brittleness.
💡 What Is Enhanced Chain-of-Thought Prompting?
Enhanced-CoT is an upgrade to standard CoT prompting.
In regular CoT, you give the model one example of step-by-step reasoning to guide its thinking.
In Enhanced-CoT, you give multiple distinct rationales for the same problem… each using a different approach, tone, or line of thought.
Think of it like this:
“Here’s how a math teacher would solve it.
Here’s how a logic puzzle lover would do it.
And here’s how a clever student might shortcut their way through.”
Each path lands on the same answer, but gets there differently.
You’re not just teaching the model what to think.
You’re teaching it how to think in different ways.
🧠 How Enhanced CoT Works (Step-by-Step)
Let’s break down the mechanism behind the magic:
1️⃣ Multiple Rationales Per Example
Instead of giving the model one worked example, you give 3–5 varied reasoning paths for the same question.
Each example uses:
Different structures
Different assumptions
But the same final outcome
2️⃣ The Model Learns to Generalize, Not Just Repeat
LLMs are pattern recognizers.
By seeing that different reasoning paths can lead to the same result, the model internalizes abstract reasoning patterns, not just surface-level formats.
It starts to recognize that:
There are multiple ways to arrive at the truth
Logic can take many forms
Thought isn’t always linear
3️⃣ It Adapts to New, Unfamiliar Problems
When prompted on a new task, the model can now:
Mix and match reasoning styles
Handle tasks that don’t match its training format
Recover when one line of logic breaks down
This flexibility is especially useful for:
Harder, multi-step problems
Real-world edge cases
Prompts that involve ambiguity or missing info
🔍 Why Enhanced CoT Works So Well
Now that we understand how it works… let’s talk about why it works.
✅ 1. It Teaches Flexibility Over Format
Standard prompting often teaches LLMs a “template.”
Enhanced-CoT shows the underlying concept across multiple templates.
This helps the model reason more like a human: who adapts, reframes, and problem-solves from different angles.
🧠 2. It Mirrors How Humans Learn Best
There’s a name for this in education: varied practice.
We retain ideas better when we learn them in different ways: visual, verbal, analogical, logical.
Enhanced-CoT brings this same diversity to the model’s thinking process.
🛡️ 3. It Makes AI More Robust to Failure
In regular CoT, if the model follows a flawed reasoning path, the output collapses.
In Enhanced-CoT, the model has seen alternate approaches. So it can pivot more easily, or even blend methods to create a stronger chain of logic.
This makes the model more resilient to:
Prompt ambiguity
Unfamiliar tasks
Noisy or incomplete information
📈 4. It Boosts Performance Where It Matters
This has also been backed by serious research, and not just someone’s opinion.
According to the Ko et al. (2023) paper A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT:
Enhanced-CoT outperformed standard CoT on math word problems, logic reasoning, and symbolic tasks.
Accuracy improvements were especially strong on harder examples, the kind where brittle logic often fails.
And here’s the best part: this boost came without any model fine-tuning. Just smarter prompting.
In other words, Enhanced-CoT lets you teach your LLM to think better… with nothing more than a well-crafted input.
🛠️ How You Can Use Enhanced CoT Yourself
This technique isn’t just for researchers. It’s for anyone using LLMs to solve problems, write content, or reason out loud.
Here are some practical ways to apply it:
🔧 1. Strategy and Problem-Solving
“Give me 3 ways to improve user retention: one based on product UX, one on behavioral nudges, one on pricing incentives.”
✍️ 2. Writing and Creativity
“Write 3 different intros to this post: one emotional, one analytical, one story-based.”
🧪 3. Teaching and Explanation
“Explain the concept of blockchains from the perspective of a finance analyst, a school teacher, and a 12-year-old.”
You’re not just prompting for variety.
You’re prompting for mental agility… and that’s where the best ideas come from.
🎯 Final Thought: Don’t Just Ask for One Brain. Prompt for a Think Tank.
Chain-of-Thought prompting showed us that LLMs can reason.
Enhanced-CoT shows us that they can reason like humans… trying multiple paths, exploring perspectives, and adapting when things don’t go to plan.
And in a world where complexity is only growing, flexibility is the real superpower.
So next time you’re stuck on a problem, don’t just ask ChatGPT to solve it.
Ask it to think like five different people.
Then choose the best one.
Next on LLMentary:
So far, I hope you’ve understood (and appreciated) the power of a good prompt to extract the most intelligence out of your LLM just by talking to it better.
Next, we’ll explore more exciting methods (beyond just prompting) to further ‘Fine-Tune’ your LLMs to get more personalized results, understand how to feed your models with real-time external data sources with the RAG framework, and much more!
Until then, stay curious.
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