This content originally appeared on Level Up Coding - Medium and was authored by Harish Siva Subramanian
The technical mechanism behind one of the most powerful prompting techniques
In 2022, Google researchers published a paper that would fundamentally change how we interact with large language models. The technique was deceptively simple: just add “Let’s think step by step” to your prompts. The results were extraordinary — accuracy improvements of up to 40% on complex reasoning tasks.
This wasn’t magic. It was Chain-of-Thought (CoT) prompting, and understanding why it works reveals something profound about how LLMs actually process information.
The Problem: Autoregressive Bottleneck
To understand Chain-of-Thought, we first need to understand the fundamental constraint of transformer-based language models.
LLMs generate text autoregressively — one token at a time, left to right. Each token is predicted based on all previous tokens. Critically, once a token is generated, the model can’t go back and revise it based on tokens that come later.
This creates a problem for complex reasoning. Consider this math word problem:
“A restaurant has 23 tables. Each table seats 4 people. If 95 people arrive, how many will be left standing?”
The “obvious” answer format is: “X people will be left standing.”
But to generate that X, the model needs to:
- Parse the problem structure
- Identify the relevant operations (multiply, subtract)
- Execute the calculation (23 × 4 = 92, then 95–92 = 3)
- Format the answer
In a direct answer, all this computation must happen in the model’s internal activations between reading the question and producing the first token of the answer. That’s a lot of computational work compressed into a single forward pass.
The Solution: Externalizing Computation
Chain-of-Thought prompting fundamentally changes this dynamic by externalizing the reasoning process. Instead of computing everything internally, the model generates intermediate reasoning steps as tokens in the output sequence.
Here’s what happens with CoT:
Prompt: “Let’s solve this step by step: A restaurant has 23 tables…”
Model output:
First, I need to find the total seating capacity.
- Number of tables: 23
- Seats per table: 4
- Total capacity: 23 × 4 = 92 seats
Now I can find how many people are left standing.
- People arriving: 95
- Available seats: 92
- People standing: 95 - 92 = 3
Therefore, 3 people will be left standing.
By generating this intermediate text, the model gets to “think” across multiple autoregressive steps. Each intermediate conclusion becomes part of the context for subsequent tokens, effectively expanding the model’s working memory.
Why This Works: The Mechanics
The effectiveness of Chain-of-Thought stems from three core mechanisms:
1. Sequential Computation Allocation
Transformers have a fixed compute budget per forward pass (determined by model depth and width). When forced to answer directly, all reasoning must fit within this budget.
CoT distributes this computation across multiple forward passes. Step 1’s output becomes part of Step 2’s input context, allowing the model to build on previous conclusions rather than computing everything simultaneously.
This is analogous to the difference between solving a complex integral in your head versus on paper — the paper becomes an external memory that lets you tackle problems beyond your working memory capacity.
2. Attention Pattern Optimization
Research on transformer interpretability reveals that attention heads specialize in different types of relationships (positional, semantic, syntactic). When reasoning is explicit in the token sequence, these attention patterns can operate more efficiently.
For instance, when the model generates “23 × 4 = 92”, later tokens can attend to that intermediate result directly. Without CoT, the model must maintain this information implicitly in the activation space, which is less reliable and harder to route through subsequent layers.
3. Implicit Constraint Satisfaction
Complex reasoning often involves satisfying multiple constraints simultaneously. CoT allows the model to address constraints sequentially rather than all at once.
Consider a logic puzzle: “All cats are animals. Some animals are pets. Therefore…”
With direct answering, the model must map from premises to conclusion in one shot. With CoT, it can:
- First establish the logical structure
- Then identify the valid inference pattern
- Finally generate the conclusion
Each step constrains the solution space for the next, creating a more reliable reasoning path.
Zero-Shot vs Few-Shot CoT
The original breakthrough came in two forms:
Zero-Shot CoT: Simply appending “Let’s think step by step” or “Let’s solve this problem” triggers step-by-step reasoning without examples.
Few-Shot CoT: Providing examples of step-by-step reasoning before the actual question, teaching the model the desired reasoning pattern.
Zero-Shot CoT is remarkable because it suggests these reasoning traces were already latent in the model’s training — we just needed the right prompt to elicit them. The model has seen countless examples of humans working through problems step-by-step in its training data.
The Self-Consistency Extension
Researchers soon discovered a powerful extension: generate multiple reasoning chains and take the majority vote on final answers.
Self-Consistency Algorithm:
- Generate 5–10 different reasoning chains for the same problem
- Extract the final answer from each chain
- Return the most common answer
This works because different reasoning paths may make different mistakes, but errors are less likely to be consistent across multiple independent chains. Correct reasoning tends to converge on the same answer.
This technique can improve accuracy by another 10–20% beyond standard CoT, though at the cost of multiple inference calls.
Implementation Patterns
Here are practical patterns for implementing CoT in production systems:
Pattern 1: Explicit Step Markers
Analyse this problem step by step,
Step 1 - Understand the question:
[model generates understanding]
Step 2 - Identify relevant information:
[model generates extraction]
Step 3 - Apply reasoning:
[model generates logic]
Step 4 - Formulate answer:
[model generates conclusion]
Pattern 2: Socratic Prompting
Let’s break this down by answering these questions,
Q: What is being asked?
A: [model generates]
Q: What information do we have?
A: [model generates]
Q: What's the relationship between these?
A: [model generates]
Therefore: [model generates final answer]
Pattern 3: Structured Reasoning
Reasoning format:
- Given: [facts from problem]
- Goal: [what we need to find]
- Strategy: [approach to use]
- Execution: [step-by-step work]
- Conclusion: [final answer]
When CoT Doesn’t Help
Chain-of-Thought isn’t universally beneficial. It’s most effective for:
- Multi-step reasoning problems
- Mathematical word problems
- Logical deduction tasks
- Complex information synthesis
It provides minimal benefit for:
- Simple factual recall (“What’s the capital of France?”)
- Pattern matching tasks
- Creative generation where “correctness” is subjective
- Tasks already solvable with direct prompting
In fact, for simple questions, CoT can introduce unnecessary verbosity and latency.
The Computational Cost Trade-off
CoT generates significantly more tokens — often 5–10x more than direct answers. This has real implications:
Latency: More tokens = more time to generate (though streaming mitigates this) Cost: API pricing is usually per token, so CoT is proportionally more expensive Context: Longer reasoning chains consume more of your context window
For production systems, you need to balance accuracy gains against these costs.
One approach is adaptive CoT: use simple prompting first, and only invoke CoT if confidence is low or if the task is known to require reasoning.
The Future: Implicit Chain-of-Thought
Recent research explores training models to perform Chain-of-Thought reasoning implicitly — maintaining the accuracy benefits without generating verbose intermediate steps.
Techniques like STaR (Self-Taught Reasoner) train models to generate reasoning chains during training but compress them during inference. The model learns to encode the reasoning pattern in its weights rather than explicit tokens.
This could give us the best of both worlds: the reasoning capability of CoT with the efficiency of direct answering.
Practical Takeaways
If you’re building with LLMs, here’s what matters:
- Use CoT for complex reasoning tasks where accuracy is critical and cost is secondary
- Experiment with prompt phrasing — “Let’s think step by step” vs “Let’s solve this carefully” can yield different results
- Consider self-consistency for high-stakes decisions where you can afford multiple inferences
- Monitor the accuracy-cost trade-off — sometimes a more capable model with direct prompting beats CoT on a smaller model
- Parse intermediate steps — the reasoning chain itself contains valuable information for debugging and validation
Why This Matters
Chain-of-Thought prompting reveals something fundamental: LLMs are not just pattern matchers or simple statistical models. They’re capable of genuine multi-step reasoning when given the architectural affordance to do so.
The fact that “Let’s think step by step” unlocks latent reasoning capability suggests these models have learned abstract reasoning patterns from their training data. We’re not teaching them to reason — we’re providing the scaffold to express reasoning they already possess.
Understanding this mechanism doesn’t just make you better at prompting. It reveals the actual computational architecture of how these systems solve problems, and points toward how they might evolve.
The next time you see an LLM give a wrong answer to a complex question, try asking it to think step by step. You might be surprised what changes when computation is spread across tokens rather than compressed into activations.
References:
- Wei et al. (2022) — “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”
- Wang et al. (2022) — “Self-Consistency Improves Chain of Thought Reasoning in Language Models”
- Kojima et al. (2022) — “Large Language Models are Zero-Shot Reasoners”
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Understanding Chain-of-Thought Prompting: Why Making AI “Think Out Loud” Actually Works was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding - Medium and was authored by Harish Siva Subramanian
Harish Siva Subramanian | Sciencx (2025-11-11T22:41:37+00:00) Understanding Chain-of-Thought Prompting: Why Making AI “Think Out Loud” Actually Works. Retrieved from https://www.scien.cx/2025/11/11/understanding-chain-of-thought-prompting-why-making-ai-think-out-loud-actually-works/
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