Feature
Imagine navigating a twisting maze without a plan — dead ends, wrong turns and frustration are inevitable. Tackling complex business problems without step-by-step thinking feels the same. Assumptions and guesses often lead to wasted effort and incomplete solutions.
In business, leaders face similarly complex market challenges that demand quick tactics and intelligent strategies. Large language models (LLMs) offer a powerful opportunity to outsource cognitive tasks, but they have limitations. Asking for simple answers to complex questions can produce inaccurate or hallucinated outputs — fabricated responses that sound convincing but lack substance.
Enter chain-of-thought (CoT) prompting. CoT helps AI models reason transparently and systematically, so the final answer isn’t a lucky guess but a well-thought-out conclusion. Here’s how to understand CoT to improve LLM prompting and inform better decision-making.
Table of Contents
- What Is Chain-of-Thought Prompting?
- Types of Chain-of-Thought Prompting
- How Does Chain-of-Thought Prompting Work?
- Chain-of-Thought Applications
- Chain-of-Thought Benefits
- Chain-of-Thought Limitations
- Chain-of-Thought Examples
- CoT Differences: Prompt Chaining vs. Few-Shot Prompting
What Is Chain-of-Thought Prompting?
Chain-of-thought (CoT) prompting is a technique that improves LLM performance by guiding models to reason step by step before arriving at an answer. Instead of tackling complex problems simultaneously, CoT prompts the model to break challenges into smaller, logical steps — much like how humans approach complex tasks. This iterative approach enables the model to deconstruct complex problems into smaller, logical steps, where each part of the answer builds on the previous one.
This method allows models to “think themselves through” highly complex problems, reduce distractions and achieve significantly higher accuracy. The foundational CoT research paper “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" by Wei et al. was published in 2022. In a comprehensive study on CoT, Google found that it improves performance on multi-step reasoning tasks by over 30% on some functions.
While CoT is a prompting technique, newer models, like OpenAI’s o1, use CoT principles in model training and reinforcement learning. This implementation of CoT on the base level of the model has significantly improved accuracy on complex, computation-heavy queries, according to OpenAI.
While advanced models increasingly incorporate CoT during training, most existing models benefit from CoT prompting. In fact, research across CoT methods suggests that chain-of-thought prompting becomes more effective the larger the model, suggesting it will become an increasingly important method.
Related Article: 14 Top Prompt Engineering Certifications
Types of Chain-of-Thought Prompting
While the core principles of chain of thought (CoT) are straightforward, there are various ways they can be applied. Here’s a breakdown of the key types and practical considerations in order of complexity.
1. Zero-Shot Chain-of-Thought Prompting
First, what is “zero-shot” prompting?
Zero-Shot Prompting
In early applications of language model prompting, users provided examples to guide the model’s output — this was called few-shot prompting. However, as language models evolved, sophisticated models like ChatGPT could generate answers intuitively without relying on examples. This method — known as zero-shot prompting — requires the model to perform tasks "cold," using only its pre-trained knowledge.
While few-shot prompting remains essential for specific tasks, zero-shot prompting has become increasingly effective for many modern models.
Zero-Shot Chain of Thought (CoT)?
Zero-shot chain of thought combines zero-shot prompting with the structured reasoning of CoT. Introduced in “Large Language Models are Zero-Shot Reasoners” by Kojima et al. in 2022, zero-shot CoT prompts models to self-generate step-by-step reasoning for a query, improving clarity and accuracy.
How Does It Work?
The method is simple: append instructions like “Think step by step” or “Explain your reasoning” to a query.
Example
Source: Kojima et al, 2022
Without CoT: The model attempts to guess the answer outright and gets it wrong.
With CoT: By reasoning step by step, the model arrives at the correct answer.
Note: While modern models would likely solve this example without CoT, the technique remains valuable for enhancing any LLM’s ability to “think out loud.”
When to Use Zero-Shot CoT
Limited Context: The user needs prior examples or step-by-step knowledge to provide guidance.
Expanded results: Adding “Think step by step” prompts immediate generation of detailed reasoning.
Considerations
- Errors in one step can compound through subsequent reasoning, leading to an inaccurate final result.
- Remember that AI does not "reason" like humans. It predicts answers based on patterns in its training data rather than experiential logic.
- It is best suited for more straightforward tasks or queries with unnecessary explicit, well-structured examples.
2. Few-Shot Chain-of-Thought Prompting
A few-shot chain-of-thought prompt enhances an AI’s reasoning ability by including example tasks and their solutions in the prompt. These examples act as templates, demonstrating how to deconstruct a solution into logical reasoning steps. The model uses the underlying reasoning pattern to solve similar tasks.
How Does It Work?
- Provide one or more example questions (prompts) and their solutions (reasoning paths).
- The AI emulates the demonstrated step-by-step reasoning to solve the new task.
Example
In this few-shot approach, the model receives a task prompt and an example prompt-answer pair that illustrates the expected reasoning process.
In the above example, the example task provides a process-based approach to inform the subsequent task.
When to Use Few-Shot CoT
Few-shot CoT shines when:
- Handling Complex Tasks: Provided examples demonstrate structured reasoning for the model to emulate
- Structured Outputs Needed: Examples allow the AI to infer formatting and other processes
Considerations
- Quality of Examples: Poorly structured or overly specific examples can bias the model’s output.
- Resource Demands: Few-Shot CoT increases computational overhead and token usage compared to zero-shot CoT. Balancing precision with efficiency is key.
3. Multimodal Chain-of-Thought Prompting
Multimodal CoT extends chain-of-thought reasoning beyond text-based inputs, enabling AI systems to analyze and synthesize data from diverse formats such as text, images and audio.
As described in “Multimodal Chain-of-Thought Reasoning in Language Models” by Zhang et al. in 2022, multimodal CoT is particularly effective for tasks that require interpretation across data types, such as business analytics, diagnostics or multimedia-based problem-solving.
Note: Since the paper's release, models like ChatGPT and Claude have been multimodal by default. Multimodal CoT improves the models’ cross-modality thinking approach and reduces hallucinations.
Example: Analyzing a Business Chart
Imagine a user uploads a sales performance graph to an LLM and asks:
“What does this trend suggest for Q3 revenue growth?”
Baseline (Text-Only):
Without the ability to process visual data effectively, the AI relies solely on pre-trained knowledge. Non-text inputs like graphs are ignored, increasing the risk of hallucinations — irrelevant or incorrect conclusions — because the model cannot “see” or analyze the chart.
With Multimodal CoT (e.g., “vision”):
The AI processes the visual details of the graph and follows step-by-step reasoning instructions to generate an accurate analysis.
Step-by-Step Analysis:
- Identify trends in the graph, such as revenue peaks, drops or steady growth.
- Cross-reference observed trends with external factors, such as seasonal market patterns or economic conditions.
- Predict Q3 revenue and recommend actionable strategies to capitalize on trends (e.g., expanding high-performing areas or addressing declining segments).
Conference
Sep
16
HR Tech Conference Las Vegas 2025
Register
Conference
Oct
27
Gartner HR Symposium/Xpo Orlando 2025
Register
Outcome:
By integrating the graph’s visual data with logical reasoning, the AI delivers precise, actionable insights while minimizing the risk of hallucinations.
Considerations
Computation costs will be higher due to the simultaneous processing of multiple data streams.
Accuracy hinges on robust training datasets and precise prompting instructions that effectively integrate multimodal information.
4. Auto Chain-of-Thought Prompting
Auto-CoT automates the creation of step-by-step reasoning examples to improve chain-of-thought prompting. Introduced in “Automatic Chain of Thought Prompting in Large Language Models” by Zhang et al. in 2022, this method generates structured reasoning paths for groups of related queries, ensuring scalability and logical consistency.
How It Works
The process involves three steps:
Step 1: Query Clustering
Group related questions into thematic clusters to reflect diverse problem types.
Example: Customer Support System
- Cluster 1: Billing Issues (e.g., refund requests, incorrect charges)
- Cluster 2: Subscription Management (e.g., cancellations, payment updates)
- Cluster 3: Account Access (e.g., login issues, password resets)
Step 2: Generate Reasoning Paths
Use zero-shot CoT prompting (e.g., “Let’s think step by step”) to create structured reasoning steps for representative queries within each cluster.
Example Reasoning Paths:
- Cluster 1: “How do I request a refund for an incorrect charge?”
- Steps: Verify account → Identify disputed charge → Cross-reference policies → Process refund.
- Cluster 2: “How can I cancel my subscription?”
- Steps: Confirm subscription status → Initiate cancellation → Notify customer.
- Cluster 3: “How do I reset my password?”
- Steps: Verify identity → Send reset instructions → Confirm reset.
Step 3: Few-Shot Demonstrations
Combine reasoning paths into a few-shot prompt to guide the model’s response for new queries.
New Query: “How can I update my payment method for a subscription?”
Few-Shot Prompt Examples:
- “How do I request a refund for an incorrect charge?”
- Steps: Verify → Identify → Process.
- “How can I cancel my subscription?”
- Steps: Confirm → Cancel → Notify.
- “How do I reset my password?”
- Steps: Verify → Send → Confirm.
Model Response:
- Verify subscription details and current payment method.
- Provide instructions for updating payment details.
- Confirm successful update.
Source: Zhang et al, 2022
When to Use Auto-CoT
- Scalable Automation: Ideal for repetitive reasoning workflows like customer support, FAQ generation or troubleshooting systems.
- Diverse Query Sets: Effective for broad tasks involving varied but thematically related queries with shared logical structures.
Considerations
- Operational Costs: Generating clusters and automated reasoning paths increases resource demands, especially for large datasets or complex prompts.
- Model Bias: Auto-CoT relies on high-quality input data and clustering logic. Poor clustering or biased examples may reduce the model’s generalization ability.
Related Article: Has AI Already Replaced the Need for Prompt Engineers?
How Does Chain-of-Thought Prompting Work?
Chain-of-thought (CoT) prompting transforms complex tasks into manageable, step-by-step processes. By breaking problems into smaller parts, CoT enables large language models (LLMs) to produce structured, logical outputs that collectively lead to accurate solutions.
Humans solve problems using diverse strategies, such as structured reasoning, intuitive leaps or experience-based decisions. While LLMs don’t “reason” like humans, CoT prompting guides them to mimic logical problem-solving by mapping out their thought processes.
This structured approach:
- Reduces Errors: Breaking problems into smaller steps minimizes logical mistakes.
- Boosts Accuracy: Stepwise reasoning leads to more reliable answers for reasoning-intensive tasks.
- Improves Clarity: Intermediate steps help articulate how the model arrived at its conclusion.
Example: Scheduling a Team Meeting
Let’s illustrate the difference CoT can make:
Without Chain of Thought
Prompt: “What’s the best way to schedule the annual team meeting?”
Model Response: “Book a conference room.”
With Chain of Thought
Prompt: “What’s the best way to schedule the annual team meeting? Work step by step.”
Model Response:
- Check everyone’s availability.
- Find a convenient date and time.
- Select an appropriate location.
- Send invites and reminders.
Instead of a rushed, oversimplified answer, the LLM generates a detailed plan.
Why It Works:
This stepwise approach is the backbone of CoT, enabling LLMs to tackle various challenges, from simple scheduling queries to advanced calculations and strategic decision-making. By prompting the model to “think step by step,” CoT helps the AI produce transparent, accurate and practical outputs.
Chain-of-Thought Applications
Chain-of-Thought (CoT) prompting has applications across industries and functions. Below is a high-level overview of each CoT method, its description and its ideal use case:
| Chain-of-Thought Method | Description | Best Use Case |
| Zero-Shot CoT | AI reasons step-by-step without examples. | Simple tasks, limited context. |
| Few-Shot CoT | Guided with structured examples. | Complex, structured outputs. |
| Multimodal CoT | Combines text, images, or other data. | Cross-modality tasks. |
| Auto CoT | Automated creation of reasoning paths. | Scalable, repetitive tasks. |
Industry Examples
- Decision Support: Help leaders make strategic decisions by dissecting them into logical steps.
- Customer Support: Guide chatbots in troubleshooting systematically rather than offering generic fixes.
- Data Analysis: Handle multi-step reasoning for forecasting, budgeting or reporting.
- Education: Deliver step-by-step problem-solving guidance for students and professionals.
Chain-of-Thought Benefits
CoT prompting fundamentally transforms how LLMs approach complex reasoning tasks, offering several research-backed advantages.
- Quantifiable Accuracy Improvements: As demonstrated in Google's research, CoT enhances performance on multi-step reasoning tasks by over 30% by preventing cumulative errors that typically occur when models attempt to solve complex problems in a single step.
- Enhanced Debugging Capabilities: Unlike black-box responses, CoT's intermediate reasoning steps allow users to identify exactly where potential errors occur in a reasoning chain, making it easier to refine prompts or correct faulty assumptions.
- Domain-Agnostic Application: While initially developed for mathematical reasoning, CoT has proven effective across diverse fields ― from analyzing multimodal business data to automating customer support workflows through techniques like Auto-CoT clustering.
Chain-of-Thought Limitations
While CoT is a powerful tool, it comes with inherent challenges.
- Error Propagation: Models are highly effective at following CoT steps, but if a step is incorrect, the error can easily create inaccurate outputs.
- Computational Costs: Multi-step reasoning increases resource usage, consuming more tokens and processing power.
- Complexity Overhead: Overusing CoT for simple tasks can unnecessarily complicate outputs, requiring ongoing maintenance of prompts.
- Reliance on Prompt Quality: Effective reasoning depends heavily on clear, high-quality prompts.
Chain-of-Thought Examples
Here is how CoT transforms tasks:
Example 1: Market Segmentation
Prompt: I need to segment a market. Who should I target?”
Answer Without CoT:
“Target B2B clients.”
Prompt: I need to segment a market. Who should I target? Work step by step.”
Answer With CoT:
- Evaluate industry trends
- Analyze company size and geographic location
- Prioritize high-value segments based on data
Conclusion: “Given the evaluation of industry trends, company size and geographic location, focus on high-potential B2B verticals such as…”
Example 2: Feature Prioritization
Prompt: “Which feature should we build first?”
Answer Without CoT:
“Build feature X first.”
Prompt: “Which feature should we build first? Work step by step.”
Answer With CoT:
- Consider user feedback scores
- Assess development efforts
- Align priorities with business goals
Conclusion: “Based on user feedback, development effort and alignment with business goals, feature X should be prioritized to deliver maximum impact.”
CoT Differences: Prompt Chaining vs. Few-Shot Prompting
Chain-of-thought (CoT) is one of many prompting techniques that can be used individually or in combination with others. Two commonly paired techniques — prompt chaining and few-shot prompting — are often used alongside CoT but serve distinct purposes.
Prompt Chaining
Prompt chaining connects multiple prompts sequentially, where the output of one prompt becomes the input for the next. This technique benefits tasks requiring iterative workflows, extended problem-solving or creative brainstorming.
Key Use Cases:
- Iterative workflows (e.g., refining project deliverables)
- Step-by-step creative tasks (e.g., drafting and expanding plans)
Example: Writing a Detailed Project Plan
Prompt 1: “Generate project goals.”
Model Output: A list of high-level project goals.
Prompt 2: “Expand on {Prompt 1 Goals} with specific action steps.”
Model Output: Detailed tasks aligned with each {Prompt 1 Goal}.
Few-Shot Prompting
Few-shot prompting provides explicit examples within a prompt to guide the model’s reasoning.
Key Use Cases:
- Tasks requiring structured or standardized outputs
- Situations where demonstrations improve model performance (e.g., logical reasoning, calculations)
Example: Generating Customer Support Responses
Prompt: “Here are examples of how to answer customer queries. Use this format to answer this question: “How do I cancel my subscription?”
Example 1:
Query: “How do I reset my password?”
Response: “Go to settings → click "Forgot Password" → follow the email instructions.”
Example 2:
Query: “How can I update my payment method?”
Response: “Navigate to billing settings → select "Update Payment Method" → save your changes.”
Model Output: “Go to billing settings → select "Cancel Subscription" → confirm the cancellation.”
