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What is Chain of Thoughts? How it works?

Chain of Thought (CoT) prompting is a technique in artificial intelligence (AI) reasoning that is gaining widespread recognition. It enables AI to solve complex tasks by breaking them down into smaller, logical steps.

Instead of delivering an answer directly, models following CoT prompting provide a more thoughtful and transparent solution process, similar to how we humans solve problems. For more complex problem-solving approaches using AI, check out Reinforcement Learning from Human Feedback.

Traditionally, AI models were designed to predict outcomes based on patterns, but they often struggled with more complex problems that required reasoning. CoT changes that by teaching AI to break down tasks, similar to how a person might solve a problem by considering each step along the way.

The release of OpenAI’s new model o1 has made Chain of Thought (CoT) prompting a hot topic. This new model enhances AI’s ability to handle complex tasks by reasoning through problems step-by-step, offering clearer, more accurate responses in areas like education, healthcare, and programming. This development also underscores the shift toward AI models capable of custom tasks, similar to those discussed in how to deploy custom AI agents to your website.

What is Chain of Thought Prompting?

Chain of Thought (CoT) prompting is an advanced technique that enhances how AI models handle complex tasks. Unlike traditional methods, which often rush to an answer, CoT guides the model through a step-by-step reasoning process. This breaks down problems into manageable components, making the AI’s decision-making more transparent and reliable, especially for tasks requiring multiple steps.

For example, in solving a math problem, a traditional AI model might directly calculate the final answer. In contrast, Chain of Thought prompting ensures the AI explains each intermediate step, allowing for error detection and correction. This step-by-step reasoning is essential for tasks like multi-step math problems, commonsense reasoning, and natural language understanding. This will further help GPT chatbots performing dynamic actions in realtime.

The concept of Chain of Thought prompting was introduced by Google researchers in 2022. Their studies showed that this approach significantly improved AI accuracy in areas like mathematical problem solving and logical reasoning.

Types of Chain of Thought Prompting

Chain of Thought (CoT) prompting breaks down problems into steps, improving AI performance. Understanding the types of CoT prompting is crucial for optimizing AI across different tasks. Common types include:

1. Zero-Shot Chain of Thought (Zero-Shot CoT)

Zero-Shot CoT prompts the model to solve a problem without any prior examples, relying entirely on its internal reasoning capabilities.

Example:

Problem: What is the sum of all odd numbers between 1 and 20?
Zero-Shot CoT Output:
The model lists odd numbers (1, 3, 5, 7…) and then sums them step-by-step, explaining that odd numbers are not divisible by 2 and why each number qualifies, before arriving at 100.

This method works best for straightforward tasks where no prior guidance is needed.

2. Few-Shot Chain of Thought (Few-Shot CoT)

Few-Shot CoT provides a few examples of how to break down a problem before asking the model to solve a similar task.

Example:

Problem: Calculate the final price of an item after applying multiple discounts.
Few-Shot CoT Setup:
- $100 item, 20% discount → $80
- $200 item, 10% discount → $180
Model Task: Now calculate the price for a $150 item with a 15% discount.
Model Output:
Using the examples, the model calculates the price as $127.50 by following the discounting steps from the examples.

This approach is ideal for tasks requiring layered reasoning, such as mathematical or logical deduction.

3. Automatic Chain of Thought (Auto-CoT)

Auto-CoT allows the model to generate reasoning steps automatically, constructing its own logical sequence as it processes the task.

Example:

Problem: A factory produces 100 items per day. The factory increases its production by 10 items each day. How many items are produced after 7 days?
Auto-CoT Output:
- Step-by-Step Calculation:
  - Day 1: 100 items
  - Day 2: 110 items
  - Day 3: 120 items
  - Continue calculating for each day…
- Total Production After 7 Days:
  Finally, it computes the total production over 7 days: 910 items.

4. Multimodal Chain of Thought (Multimodal CoT)

Multimodal CoT integrates text and visual data to allow models to reason across multiple inputs, such as images and language. It can be used like this below example.

Example:

Task: Analyze this chest X-ray and the patient’s symptom report to determine potential causes of chronic coughing.
Multimodal CoT Output:
- Visual Input (X-ray):
  The model identifies abnormalities like lung shadowing or signs of fibrosis.
- Textual Input (Symptom Report):
  The model processes the symptoms—chronic coughing, difficulty breathing, and history of smoking.
- Reasoning Process:
  The model correlates visual lung abnormalities with the symptom data, suggesting possible conditions such as Chronic Obstructive Pulmonary Disease (COPD) or lung cancer.
- Recommendation:
  The model advises further tests, such as CT scans or sputum analysis, based on the initial multimodal reasoning.

Advantages of Chain of Thought (CoT) Prompting

Chain of Thought (CoT) prompting is a highly effective technique for improving the reasoning capabilities of large language models (LLMs). It offers distinct advantages, making it a valuable in applications that require logical, step-by-step thinking. Below are the core benefits of implementing CoT prompting:

1. Enhanced Accuracy

One of the major advantages of CoT prompting is its ability to improve accuracy. By breaking down complex tasks into smaller, sequential steps, it ensures that the model follows a logical progression, significantly reducing the chances of skipping important intermediate steps.

Why it matters: Traditional models tend to rush to an answer, sometimes overlooking crucial elements. CoT prompting mitigates this by forcing the model to work through each piece of the problem.
Where it’s effective: This method shines in fields requiring multi-step reasoning, such as mathematical problem solving, data analysis, and complex question-answering tasks.

2. Reduction of Bias

Another important advantage of CoT prompting is its capacity to reduce biases. Traditional LLMs can sometimes produce biased or one-sided results based on the dataset they’ve been trained on. CoT prompting, by requiring transparent reasoning steps, can help identify and correct biased assumptions early in the decision-making process.

Impact on outputs: When a model is prompted to justify its decisions, it becomes easier to spot biased conclusions and revise them. This helps produce fairer, more balanced results.
Applicable domains: This benefit is particularly valuable in sectors such as high quality synthetic dataset generation, legal reasoning, legal reasoning, financial predictions, and analysis, where unbiased results are most important.

3. Human-Like Decision Making

CoT prompting mirrors human reasoning by encouraging the model to think through problems in a structured, step-by-step manner, similar to how humans process information. This approach not only helps the model reach more accurate conclusions but also makes its decision-making process more interpretable.

Benefit: Instead of simply producing an answer, the model provides a transparent reasoning trail, making it easier for users to understand how a decision was reached.
Use cases: This is particularly useful in areas such as business analytics, strategic planning, and policy-making, where understanding the “why” behind a decision is just as important as the final outcome.

4. Minimizes Hallucinations

Hallucination, which happens when a model generates incorrect or irrelevant information is still a common issue with language models. CoT prompting can help reduce this problem by ensuring logical consistency in the reasoning process. When the model needs to explain its steps, it’s less likely to invent false information.

Importance for accuracy: By ensuring that each step is logically sound, CoT reduces the chances of models veering off course and producing misleading data.
Key sectors: This is vital in high-stakes fields such as healthcare, legal analysis, and automated customer support, where errors can have serious consequences.

5. Adaptability to Various Domains

One of the great things about CoT prompting is its adaptability. It can be used in many different areas, from simple math problems to more complex tasks that involve both text and images.

Emerging trends: In areas like AI-driven tutoring, legal research, and medical diagnostics, the ability to break down and solve complex issues is highly beneficial.

Limitations of Chain of Thought Prompting

While Chain of Thought (CoT) prompting has significantly improved AI’s reasoning capabilities, it does come with certain limitations that affect its performance, especially in practical applications. Understanding these limitations is crucial for maximizing its effectiveness.

1. Model Size and Complexity Constraints

One of the biggest challenges with CoT prompting is that its success heavily depends on the size and computational capacity of the language model. Smaller models, due to limited resources and processing power, often struggle to execute effective step-by-step reasoning. These models may produce irrelevant or incomplete chains of thought, leading to inaccurate results. In contrast, more advanced models, such as GPT-4, are better equipped to handle the intricacies of CoT prompting because they have been trained on larger datasets and have more robust computational capabilities.

Smaller models often try to follow chain-of-thought (CoT) steps, but their reasoning can end up being irrelevant or shallow, which doesn’t help solve the task. This shows why it’s important to match the model size with task complexity. Larger models are needed for tasks that involve multiple reasoning steps, while smaller models may struggle to give accurate results when CoT is used.

2. Increased Computational Overhead

Another limitation of CoT prompting is the significant computational overhead it introduces. Since CoT requires models to break down tasks into multiple steps, this naturally increases the amount of processing time and memory required to generate a response. This can result in slower response times, which is problematic for real-time applications where speed is critical.

In industries that need fast results, like customer service bots or real-time data analysis, the extra time CoT processing takes can be a problem. Although CoT improves reasoning accuracy, the slower speed can hurt the user experience in situations where time is critical.

3. Diminishing Returns for Simple Tasks

A key limitation of CoT prompting is that it can be unnecessary for simple tasks. CoT is designed to help models break down and reason through complex problems, but for tasks that don’t require multiple steps, this process can be redundant. In fact, using CoT on straightforward problems may lead to longer, more convoluted answers that offer no additional value.

If the task is simple, like retrieving facts or doing basic math, CoT can make things more complicated by adding unnecessary reasoning steps.

Conclusion

Chain of Thought (CoT) prompting enhances AI reasoning by systematically breaking down complex tasks into logical steps. This method improves accuracy and reduces bias, allowing models to analyze each element of a problem. The structured approach aligns with human reasoning, making AI outputs clearer.

However, CoT prompting has limitations. Smaller models may struggle with effective reasoning due to resource constraints, resulting in incomplete outputs. The increased computational demands can slow response times, which is a concern for applications requiring quick results. Additionally, applying CoT to simple tasks may complicate the process unnecessarily.

To fully benefit from CoT prompting, it is important to optimize its use while addressing these limitations. By focusing on performance and clarity, AI systems can produce accurate and interpretable results across various fields, including education and healthcare.