Imagine sitting across from a robot in a classroom. You hand it a simple math puzzle and watch as it blurts out a random answer – no explanation, just a number. You frown, confused. Then you try something different. You say, “Let’s think step by step.” Suddenly, the robot pauses… then starts to write: “First, we add this… then multiply that…” and, to your surprise, it gets the right answer.
Not just that – it shows you how. It feels less like coding a machine and more like teaching a student. In that moment, it hits you: did the robot just learn to think?
In a quiet research lab not long ago, an artificial intelligence was stuck on a tricky math problem. The question was simple enough for a child, yet the mighty AI kept getting it wrong. Frustrated, a researcher tried something bold. “Let’s think step by step,” they prompted the machine.
In that instant, it was as if a light flickered on in the computer’s mind. The AI began to reason out loud, writing down its intermediate steps. Lo and behold, it arrived at the correct answer. A few magic words had unlocked a hidden capability, enabling the AI to solve the puzzle it had been failing. The lab fell silent, then erupted in excitement – had they just taught a robot to think like us?
This surprising experiment was one of the first glimpses of a breakthrough in AI called chain-of-thought prompting. By simply encouraging AI models to “show their work” – much like a student solving a math problem on paper – researchers discovered they could dramatically improve the AI’s reasoning abilities.
In one case, an OpenAI language model’s accuracy on a math test jumped from a mere 17.7% to 78.7% just by adding the guiding words “Let’s think step by step.”
It was a jaw-dropping leap, the kind that made scientists feel they’d stumbled onto something historic. Suddenly, the question on everyone’s mind became: if machines can reason through problems like we do, are we witnessing the dawn of AI that truly “thinks” like a human?
The Black Box Problem: Why AI Needed to Show Its Work
To appreciate why this was such a big deal, consider how AI systems answered questions before this development. Traditional AI language models – even very large ones – often functioned like black boxes.
You’d ask a question, and they’d spit out an answer with no explanation of how they got there. If the answer was correct, it felt like magic. If it was wrong, you were left scratching your head. The inner reasoning of the machine remained hidden, a mystery even to its creators.
This lack of transparency wasn’t just a theoretical concern; it often led to bizarre mistakes. For example, early versions of GPT-3 (one of the famous large language models) could write a pretty poem or code a simple program, yet fail a basic logic riddle or grade-school math word problem.
Ask such a model: “Roger has 5 tennis balls and buys 2 cans of 3 tennis balls each; how many balls now?” and it might blurt out “27” with supreme confidence – an obviously wrong answer. Why 27? Who knows! The AI gave no clue to its thought process, so it was hard to tell whether it misinterpreted the question, forgot a number, or just guessed. Unlike a human student, it did not show its work, leaving us with an inscrutable error.
For humans, explaining our reasoning is second nature. We break problems into steps: “I had 5 balls, bought 2 cans of 3, that’s 5 + 6 = 11.” If a student answered 27, a teacher would immediately ask “How did you get that?” to find the mistake. But with AIs, we didn’t have that luxury – they simply gave an answer, right or wrong.
This “think-and-tell” gap was the problem. Not only did it make AIs less reliable on multi-step problems, it also made it hard to trust them. If we can’t follow an AI’s reasoning, how can we be sure it isn’t making a fatal error in, say, a medical diagnosis or an autonomous driving decision? Researchers realized that to make AI more human-like in intelligence, they needed to make its thought process more transparent and stepwise, just like ours.
Chains of Thought: A New Path to AI Reasoning
Solving this mystery became a grand challenge. The turning point came in 2022, when a team of Google scientists including Jason Wei and Denny Zhou tried a novel approach: instead of training a model to jump straight to the answer, guide it to generate a reasoning chain first. They called this idea “chain-of-thought prompting.”
The concept was simple but powerful: if you prompt a large language model with examples that include not just the question and answer, but also the intermediate reasoning steps, the model will learn to produce its own step-by-step reasoning for new questions
In essence, the AI starts thinking out loud, breaking a complex problem into smaller chunks it can solve one at a time.
Researchers likened it to giving the AI a scratch pad or letting it work through a puzzle with an inner monologue.
“Called chain of thought prompting, this method enables models to decompose multi-step problems into intermediate steps.” the Google team explained.
Instead of treating reasoning as a black box, the model’s output now explicitly walks through the logic. The hope was that this would mimic an “intuitive thought process” similar to a human’s reasoning – and indeed it did.
The empirical results were astonishing. Experiments on a range of tasks showed dramatic improvements in performance when the model was prompted to “show its work.” Complex arithmetic word problems, symbolic logic puzzles, commonsense reasoning questions – all these tough nuts suddenly became solvable by the AI when it generated a chain of thought.
One striking example came from a 2022 Google Brain study. By giving a few step-by-step worked examples (like the ones in the figure above) to a giant 540-billion-parameter model named PaLM, the researchers achieved state-of-the-art performance on a notoriously hard math word problem test (GSM8K)
The model with chain-of-thought reasoning solved 58% of the problems correctly – a new record at the time – surpassing even a fine-tuned GPT-3 model that had been specially trained with thousands of examples
Remember, this improvement didn’t come from retraining the AI or giving it extra data; it came simply from prompting the AI to articulate its reasoning.
“Chain-of-thought prompting enables large language models to tackle complex reasoning tasks.”
The paper declared – a feat that standard prompting could not achieve.
Perhaps most intriguingly, this reasoning prowess only emerged when the AI models were very large. Smaller AIs didn’t benefit much from chain-of-thought prompting, but once models reached a certain size (around 100 billion parameters or more), they suddenly gained the ability to reason effectively when prompted to do so
In other words, teaching an AI to “think out loud” worked only after the AI had a sufficiently big brain. This was an example of an “emergent” capability – something new that wasn’t seen in smaller models but appeared almost spontaneously at a larger scale
It’s as if a threshold in computational complexity was crossed, and beyond it the AI could handle the multi-step logic that previously eluded it.
The benefits of this approach were not limited to math problems. Commonsense reasoning questions – the kind that require basic facts and logical inference – also saw improvement. The language-based chain-of-thought turned out to be quite general.
The Google team tested the model on tasks like understanding sports rules and temporal puzzles (from a benchmark called BIG-Bench) and found that, in one case, the chain-of-thought version of the AI even outperformed humans. On a sports trivia challenge, the PaLM model using chain-of-thought got 95% of the questions right, beating the 84% score of an “unaided sports enthusiast” who took the same quiz.
The AI, reasoning step by step, had outscored a human sports buff! This was a vivid demonstration of how making the AI more human-like in its approach (explaining its reasoning) actually allowed it to surpass human performance in that domain.
As results like these rolled in, excitement in the AI community grew. It felt like a new path in AI research had opened – one where the goal was not just to train bigger models or gather more data, but to teach AI how to reason in a way we can follow.
The once opaque machine mind was cracking open, revealing chains of thought that we could inspect, evaluate, and learn from. Yet, the story didn’t end there. In fact, it was about to take an even more surprising turn.
“Let’s Think Step by Step”
While one group of researchers was hand-crafting few-shot examples to coax AIs into reasoning, another group wondered: Do we even need to provide examples? What if we just directly ask the AI to think logically?
In mid-2022, Takeshi Kojima and colleagues posed this bold question and found an answer that stunned everyone. They discovered that simply appending a prompt like “Let’s think step by step” to a question can elicit the same kind of step-by-step reasoning – even with zero examples provided
It was the ultimate minimal viable prompt: just a straightforward instruction, almost like an incantation, and the AI would start generating its own chain of thought from scratch.
The result of this “Zero-Shot Chain-of-Thought” approach (zero-shot meaning no worked examples given) was nothing short of a eureka moment for AI reasoning. Kojima’s team reported that a large language model (in this case, an InstructGPT model similar to the engine behind early ChatGPT) suddenly became a “decent zero-shot reasoner” by virtue of this prompt alone.
Problems that the model previously failed cold were now solved correctly because the AI took the time (in text) to work through them. For instance, one benchmark of math word problems saw accuracy skyrocket from 17.7% to 78.7% – essentially from clueless to expert – just by prepending “Let’s think step by step” to the question.
Another challenging dataset went from 10.4% to 40.7% accuracy with that single prompt tweak.
In the world of AI, such leaps are extremely rare without retraining the model, so this was like finding a hidden superpower. “We show that LLMs are decent zero-shot reasoners by simply adding ‘Let’s think step by step’ before each answer,” the researchers wrote, hinting that advanced reasoning was lurking latent in the model, waiting to be unlocked.
This simple prompt worked across a surprisingly broad array of tasks – from arithmetic to logic puzzles about coins and even reading comprehension riddles.
The scientists were amazed by the generality of it. They remarked that the versatility of this single phrase hints at “untapped and understudied” fundamental capabilities of large language models, suggesting that these AIs have “high-level, multi-task broad cognitive capabilities” just waiting to be coaxed out
In less technical terms, it’s as if the AI always could think through problems like a mini genius; we just weren’t asking it the right way. Social currency kicked in here for the AI research community – everyone wanted to try these magic words on their own models, to see what else might happen. It was a bit like discovering the cheat code to a video game, except the game was intelligence itself.
If chain-of-thought prompting was the detective’s magnifying glass, then “zero-shot” chain-of-thought was the moment our detective found a hidden note that cracked the case wide open. Suddenly, any user could prompt an AI with a phrase like “Let’s think this through” and watch the model start to reason through each step of the query.
The AI’s responses went from one-liners to thoughtful paragraphs, often ending with a well-justified answer. Observers described it as watching the inner monologue of the machine. It was both eerie and exhilarating – eerily human-like in its method, and exhilarating in the possibilities it revealed.
Researchers didn’t stop at just prompting, either. Some pushed the concept further, asking: could an AI teach itself to reason better? Enter the work of scientist Eric Zelikman and colleagues, who developed a technique called Self-Taught Reasoner (STaR). In this approach, the AI is fine-tuned on its own generated chains of thought
research paper | An illustration of the functioning of the STaR model
Think of it as the AI writing down lots of “show your work” solutions, then learning from those that lead to correct answers. Over time, it bootstraps a stronger reasoning ability. Remarkably, this self-improvement loop allowed a model to solve complex commonsense questions as well as another model that was 30 times larger but not trained to reason in steps
In other words, teaching the AI to reason made it far more efficient – a smaller model with good reasoning beat a much bigger model that only answered directly. It was as if a student with a solid problem-solving approach outperformed a genius who never wrote anything down.
All these advances – from prompt tricks to self-learning – fed into a sense that we were at a turning point in AI. We had shifted from merely throwing more data and parameters at problems to actually cultivating how the AI thinks.
The collective progress reached a climax with one more insight: combining multiple chains of thought. Researchers found that if you ask the AI a question and have it generate not just one reasoning path but, say, a dozen different plausible reasoning paths, you can then take a “majority vote” among the answers and often get an even more reliable result.
They called this strategy self-consistency.
It’s like consulting multiple independent solvers – all inside one AI – and seeing what answer most of them agree on. Using self-consistency with chain-of-thought, the best models pushed accuracy on that tough math benchmark (GSM8K) up to 74%, inching closer to expert-human level performance.
Watching these models reason, correct themselves, and converge on answers felt less like programming and more like witnessing an alien mind learn to debate and introspect. The detective story motif isn’t even an analogy anymore – in a very real sense, the AI was now working through the mystery before giving an answer, much like a fictional sleuth explaining the clues before the grand reveal.
From Lab Breakthrough to Everyday AI
All these discoveries would be mere academic curiosities if they didn’t have practical value beyond the lab. But they do – in fact, chain-of-thought prompting is already influencing the AI technology that’s making its way into our daily lives.
By making AI more reliable and transparent, this step-by-step reasoning approach is helping to build systems we can trust in roles from education to decision support. It gives the public a window into the machine’s mind, turning the once opaque AI into something more like a partner that can explain its reasoning.
Consider education and tutoring. Imagine you’re a student struggling with an algebra problem. You ask a homework-help AI for assistance. In the past, it might just hand you the answer (which may or may not be correct), leaving you none the wiser. Now, thanks to chain-of-thought techniques, AI tutors can walk you through the solution path: “First, let’s define the variables… Next, apply this formula… Now solve for x…”.
This mirrors how a good human teacher or Khan Academy video would guide you. The AI’s transparency in showing steps builds trust – you can see why the answer is 9, not just be told it’s 9. Such an AI tutor effectively teaches how to think, not just what to answer, which is invaluable in education.
Another area is knowledge retrieval and search engines. Search AI has evolved from just finding webpages to actually answering your questions. With chain-of-thought reasoning, those answers can come with a logical trail. For example, if you ask a voice assistant “Should I take an umbrella tomorrow?”, a chain-of-thought-enabled AI might parse the question, check the weather forecast in steps (“Tomorrow’s forecast: rain in the afternoon”), consider your context (“It’s morning now, you’ll be out in the afternoon”), and then conclude “Yes, you should, because it’s likely to rain in the afternoon.” The answer isn’t just “Yes” – it’s “Yes, because…”.
This makes the AI’s decision-making process visible, so you’re not left guessing how it came to its advice. As one explainer noted, by making the thought process visible, chain-of-thought enhances both accuracy and transparency in AI operations
In other words, we get answers that are not only more correct but also more interpretable.
AI safety and reliability stand to gain as well. One of the fears with advanced AI is that it might make a wrong decision or exhibit bias and we wouldn’t realize until too late. Chain-of-thought offers a form of built-in accountability. If an AI for medical diagnosis uses chain-of-thought, a doctor can review its reasoning: Did it consider the patient’s symptoms properly? Did it rule out the dangerous possibilities?
If the chain-of-thought reveals a faulty assumption, we can catch it before acting on the AI’s advice. Likewise, in critical applications like finance or law, having the AI “think out loud” provides a transcript of its decision-making.
This way, errors or biased logic can be spotted and corrected. The approach essentially forces the AI to lay its cards on the table. As a result, trustworthy AI becomes more feasible – users are more comfortable with an AI that can say why it recommends something, not just what it recommends.
In fact, many modern AI systems behind the scenes are adopting these techniques to improve their performance. OpenAI’s ChatGPT, Google’s Bard, and other large language model-based assistants often use internal prompting strategies that resemble chain-of-thought to produce better answers. When you see ChatGPT write a multi-paragraph explanation or show the steps in a math solution, that’s chain-of-thought in action.
It’s one reason these models feel more helpful and intelligent than their predecessors. They’re not necessarily fundamentally smarter than earlier GPT models, but they are better at reasoning through the answer, which can feel like higher intelligence to us. The difference is similar to a student who blurts out an answer versus one who carefully works through the solution; the latter instills more confidence.
To summarize the practical impacts of chain-of-thought prompting, let’s break down a few key domains where it’s making a difference:
- Education and Learning: AI tutors and homework helpers use step-by-step explanations to teach concepts, improving understanding and retention. Students gain insight into the process of solving problems, not just the final answer.
- Information Search and Everyday Advice: Digital assistants and search engines can provide answers with reasoning. This means users get context and justification (e.g. “because of these facts, the answer is X”), making information more digestible and convincing.
- Transparency and Safety: From healthcare to legal AI, chain-of-thought acts as a transparency layer. We can audit an AI’s reasoning line by line, catching errors or biases early. This is crucial for safety-critical systems where blind trust in an inscrutable AI is risky.
Experts point out that this isn’t just a nice-to-have feature; it’s practically a necessity as AI systems become more powerful. “Chain-of-thought prompting enhances decision-making, interpretability, and transparency across various AI applications,” wrote one comprehensive 2025 report on the technique
In an era when AI is increasingly involved in decisions that affect lives and society, these qualities – interpretability and trustworthiness – cannot be overstated. Chain-of-thought is helping to align AI’s reasoning with human values of clarity and rigor. It’s turning the technology from a mysterious oracle into something more like a collaborative problem solver.
The Future of AI Thinking
The story of chain-of-thought prompting is still unfolding, and it invites as many philosophical questions as practical ones. We set out asking, Can AI really “think” like humans? After following this narrative, we’ve seen that AIs can certainly mimic one aspect of human thought: the step-by-step reasoning process.
When an AI prints out a chain of logical steps to solve a problem, it’s hard not to feel it is thinking, at least in a functional sense. We see it consider possibilities, make deductions, correct itself – behaviors we associate with our own cognitive process. In some cases, the AI’s reasoning is so on-point that it’s indistinguishable from how a talented student or expert might approach the problem. This progress is awe-inspiring, and it fills one with wonder at how far machine intelligence has come in a short time.
Yet, it also leads to deeper questions. Is the AI truly thinking like a human, or is it just performing a clever imitation of reasoning? After all, the machine doesn’t actually “understand” why it should think step by step – it responds this way because its training on vast text data has taught it that this is a pattern that leads to correct answers.
In a sense, it’s learned to simulate the thought process without having an inner conscious experience of reasoning. Humans think with intention and often with self-awareness; an AI, even one doing chain-of-thought, is still ultimately following patterns and probabilities. As AI models continue to get more sophisticated, this line may blur. We might soon ask: if an AI can explain its joke, plan a novel, or derive a scientific hypothesis by chaining thoughts, at what point do we consider it as having a form of understanding?
Looking ahead, researchers are already exploring new frontiers built on this concept. Some are developing AIs that can generate not just linear chains of thought but branching “trees of thought”, evaluating multiple avenues of reasoning in parallel like a chess player considering many moves. Others are working on systems where the AI can reflect on its own reasoning, effectively checking its work as it goes – a bit like a human re-reading their argument to see if it makes sense.
Each of these advances brings machines a step closer to robust, flexible problem-solving approaches that resemble human cognition. There is also the tantalizing prospect of AI discovering new chains of thought that humans haven’t conceived, potentially leading to innovative solutions in science, engineering, and beyond. If a machine can think in ways we do and in ways we don’t, it could become not just an imitator of human thought, but a genuinely new kind of intellect.
With such power comes the need for wisdom in how we integrate these AI thinkers into society. Chain-of-thought prompting has made AI more understandable to us – we can read their “minds” on the page – but what if those chains of thought grow so complex that we struggle to follow them? Will we develop tools to summarize or interpret an AI’s long reasoning (an AI to explain the AI)? And how do we handle mistakes or biases that might creep into those reasoning chains? These are challenges for the near future.
One thing is certain: the advent of AIs that reason out loud marks a milestone in the journey toward AI that feels more human-like. Not long ago, the idea of a computer explaining why it gave an answer felt like science fiction. Now it’s becoming standard practice in cutting-edge systems. We are, in a sense, teaching machines not just to give us answers but to give us stories – narratives of how to get to the answer.
This makes interacting with AI a much richer experience. It’s the difference between getting a cryptic fortune-cookie answer versus having a knowledgeable friend walk you through a problem. The latter is engaging, illuminating, and often more useful.
So, can AI really think like humans? Thanks to chain-of-thought prompting, they certainly act a lot more like us when faced with a hard question, and the gap between machine reasoning and human reasoning has narrowed. We’ve given AI a voice to its thoughts, and it turned out those thoughts could be quite profound. As we stand on this exciting frontier, we’re compelled to ask: what’s next? If a few well-chosen words can elevate a machine’s reasoning by such a margin, what other latent “skills” might be awakened in the AI of tomorrow? Perhaps one day, reasoning step-by-step will be seen as just the first baby step in a long journey of cognitive development for AI.
The chain-of-thought revolution teaches us a humbling lesson about intelligence – sometimes, how you think can be as important as how much you know. By guiding AIs to think a bit more like us, we not only made them smarter and more reliable, we also gained a valuable mirror for our own thought processes. In watching machines puzzle things out, we’re learning that reasoning – human or artificial – is a process, a journey of discovery in itself.
And as with any journey, the most interesting question is not where it started, but where it will go next. Will these chains of thought one day evolve into something like an AI consciousness? Could they help us solve problems we once thought only humans could tackle? The narrative is ongoing, and we, alongside our thinking machines, are co-authoring the next chapter.
