What if AI could solve problems like humans—step by step, with clear reasoning? That’s exactly what the Chain of Thought (CoT) model does! By breaking down complex tasks into logical steps, it makes AI more accurate and transparent.
The combination of CoT with multimodal learning and least-to-most prompting and zero-shot learning makes the system more effective. The advanced AI system DeepSeek uses this model to achieve a new level of problem-solving which transforms AI capabilities.
Chain of Thought Model – How AI Thinks
AI systems use Chain of Thought steps to split difficult problems into smaller easier tasks for solutions. The AI system solves issues one step at a time using a logic path that parallels human thinking. The method enables users to see how the AI arrives at its choices and ensures better outcomes.
The CoT technique is not as simple as just telling an LLM to “show its work”. In fact, it requires the user to provide logical problem-solving examples along with the question they are asking. This way, the LLM can arrive at a final answer by applying the provided logic.
For example, let’s say you are trying to solve a multi-step math problem. You would first establish a logical thought process by inputting:
- 1 + 2 = 3
- 3 + 1 = 4
- 4 – 2 = 1
Then, you would ask the AI to solve the following problem: 6 + 19 – 12 + 2 using the same step-by-step approach. The output would not only display the final answer (15) but also show the steps taken to arrive at it.
What is DeepSeek R1?
Deepseek R1 is an advanced AI system that utilizes the Chain of thoughts model in its reasoning and response. It isn’t just a query processing engine based on direct answers, it also does logical steps to analyze, predict, and improve accuracy.
How DeepSeek Implements Chain of Thought:
- Breaks queries into smaller parts for better understanding.
- It makes use of logical steps to evaluate each part separately.
- Reasoning techniques such as multimodal learning, least to most prompting, and zero shot learning are integrated to improve results.
- It uses reinforcement learning and model distillation to refine its answers.
DeepSeek is a powerful tool for use with complex decision making, search analysis, and AI assisted reasoning by making DeepSeek these methods.
How DeepSeek R1 Masters Reasoning – Key Chain of Thought Techniques
DeepSeek uses the Chain of Thought model with three key techniques:
Multimodal Learning:
AI’s multimodal learning is a process of using different types of data (text, images, speech, video, etc.) in order to improve decision making. The Chain of Thought model does not only rely on text based reasoning, but instead analyzes different types of inputs together to have a better understanding of the context.
Example: Suppose you upload an image of a handwritten math problem and ask DeepSeek to answer it. Using multimodal learning, the model:
- Uses the image to identify the numbers and symbols.
- It converts the handwritten text to digital form.
- Solves the equation by applying step by step reasoning (CoT).
- It explains the solution as a teacher would.
Use Cases:
- Disease Diagnosis- AI can read a patient’s medical reports in text and X-rays in images and diagnose the disease.
- Educational AI– These AI tutors can give an explanation in the form of text description or visual diagrams.
DeepSeek integrates multimodal learning to better reason over various types of inputs.
Least-to-Most Prompting:
The AI system works through small simpler subtasks before solving the full difficult problem. This is also known as progressive reasoning. The AI system divides the hard problem into smaller parts to solve and finds basic elements of the challenge. Then it solves them one by one and joins all smaller problem solutions to create the complete answer for the difficult task.
Example: Suppose you ask DeepSeek:
“How many days does it take to read a 300-page book if you read for 2 hours daily at a speed of 30 pages per hour?”
With Least-to-Most Prompting DeepSeek would create these steps to solve the problem:
- First it determines how many pages you can read in one day → 30 pages/hour × 2 hours = 60 pages/day
- Then it finds out the number of days necessary → 300 pages ÷ 60 pages/day = 5 days.
Use Cases:
- Mathematical Problem Solving– Breaking large equations into smaller calculations.
- Programming Assistance- Debugging code step by step.
The strategy involves studying separate parts of scientific research before making the final decision. DeepSeek handles problems through step-by-step processing which produces correct results instead of errors caused by complexity
Zero-Shot Learning:
Zero-shot learning helps AI systems handle new tasks that they have not practiced before. This approach depends on basic reasoning skills and logical thinking to provide right solutions.
The system handles an unknown problem since it did not undergo prior training and uses its basic problem-solving methods and what it already knows. It delivers results through its knowledge of patterns and understanding.
Example: Imagine DeepSeek has never seen a riddle like this:
“I speak without a mouth and hear without ears. I have no body, but I come alive with the wind. What am I?”
DeepSeek employs zero shot learning even without training on this exact riddle.
- Analyzes the key clues.
- It recognizes the pattern of common riddles.
- It infers the answer is ‘an echo’ (sound waves traveling through the air).
Use Cases:
- AI Translation– AI translates words that are in languages it has never been trained upon.
- General medical knowledge– Diagnosing new diseases using general medical knowledge.
- Writing stories based on no examples (AI Creativity)
This is a very method that makes A.I. very adaptable and very capable of dealing with all sorts of unexpected situations.
Conclusion
With the Chain of Thought model, AI has become more structured, explainable, and accurate in its process and reasoning of problems. It is enhanced by multimodal learning, least to most prompting, zero shot learning in DeepSeek, which enables it to answer queries across diverse domains.
An AI that combines these techniques will be still more smart and more human looking reasoning. DeepSeek’s approach is a big leap forward in making AI smarter, more reliable and more powerful for addressing the real world.
Gain insight into reasoning and non-reasoning models of DeepSeek by watching this YouTube Shorts video.
Also Read: DeepSeek vs ChatGPT: Which Is the Best AI Chatbot in 2025?
