There’s been a surge of excitement around Chain-of-Thought (CoT) prompting in large language models (LLMs). The idea is simple and seductive: if we can get an AI to “think step-by-step,” it will arrive at better, more reasoned answers — like a human would.
But here’s the catch: LLMs don’t reason — they generate.
And CoT prompting doesn’t magically grant them human logic. It just encourages them to emit a more verbose stream of tokens that look like reasoning.
Let’s unpack that.
What Is Chain-of-Thought Prompting?
Chain-of-Thought prompting is a technique where we encourage LLMs to “show their work” — to break down a problem into intermediate steps before answering. This often leads to improved performance on reasoning tasks, especially in academic benchmarks like math word problems or logic puzzles.
But here’s what’s really happening behind the scenes: the model is producing a plausible sequence of words based on patterns it has seen during training. It’s not validating logic. It’s continuing a pattern.
The Illusion of Reasoning
Let’s take a closer look at the illusion:
- More words ≠ more thought. The output might feel thoughtful because it’s long and structured. But that doesn’t guarantee accuracy.
- CoT is a style, not a safeguard. It gives an illusion of deliberation, but the model is still just doing next-token prediction.
- LLMs are fundamentally stochastic. Each response is a probabilistic guess — not a calculated logical conclusion.
As Raghu put it in the original post:
“CoT is like a student trying to explain something they don’t understand — hoping they’ll say something that accidentally gets them to the right answer.”
So, Does Chain-of-Thought Help?
Yes — but with caveats.
- CoT can improve accuracy in structured reasoning tasks.
- It can surface intermediate steps that help humans judge correctness.
- But it’s not a silver bullet for trust, logic, or explainability.
And most importantly, it doesn’t make the model truly reason — not in the human sense of evaluating premises, checking rules, and arriving at truth through deduction.
What Should We Do About It?
As AI builders and adopters, here’s what we need to keep in mind:
- Don’t confuse fluency for understanding. Just because it “sounds smart” doesn’t mean it is smart.
- Build external reasoning scaffolds. If you want logic, build workflows that include external tools, rule engines, or validators — not just verbose prompts.
- Treat LLMs as powerful generators, not decision-makers. Their strength is in suggestion and synthesis — not in judgment.
Final Thought
Chain-of-Thought prompting isn’t a lie — but it is a mirage. One that’s easy to get lost in if we mistake it for something it’s not.
Let’s keep using it where it helps — and stay clear-eyed about its limits.
