Prompt engineering has been crucial for the performance of Gen AI models like GPT-4o on various tasks. However, with the introduction of OpenAI's O1 series, this has changed. The O1 models, especially O1-preview and O1-mini, offer improved reasoning abilities and need simpler, more direct prompts for the best results. In this blog, we will compare how GPT-4o and O1 models respond to prompts, particularly for coding tasks, and explore why simpler prompts work better with O1.

The Evolution from GPT-4o to O1

GPT-4o excels in tasks that require detailed prompts and structured instructions. Its versatility makes it great for coding, general Q&A, and creative writing. However, O1’s focus on reasoning allows it to understand shorter prompts and still produce accurate, optimized results. This change reduces the need for lengthy input, making it easier for developers to work with the AI.

O1 models use "reasoning tokens" that help them think through tasks internally. This means users no longer need to break down complex tasks step-by-step, as was often required with GPT-4o. Instead, a simple prompt allows O1 to deliver accurate, nuanced responses, especially for code generation tasks.

Let’s dive into specific examples to highlight this change.

10 Examples Comparing GPT-4o and O1 Prompts

1. Writing a Factorial Function

GPT-4o Prompt:
“Write a Python function to calculate the factorial of a number. Make sure to handle edge cases like 0 and negative numbers, and include comments explaining the steps.”
O1 Prompt:
“Write a factorial function in Python.”

In this case, the O1 model automatically handles the edge cases without needing specific instructions, showcasing its advanced reasoning ability.

2. Sorting an Array with Merge Sort

GPT-4o Prompt:
“Develop a Python merge sort algorithm. Ensure it is efficient for large datasets and include comments explaining the logic.”
O1 Prompt:
“Write a merge sort algorithm in Python.”

O1 generates the same efficient, optimized code without needing specific mentions of dataset size or logic explanation.

3. Checking for Palindromes

GPT-4o Prompt:
“Write a Python function that checks if a string is a palindrome. Ensure input validation is included, and provide comments.”
O1 Prompt:
“Write a palindrome checker in Python.”

O1 understands the prompt context and includes input validation naturally.

4. Optimizing a Search Algorithm

GPT-4o Prompt:
“Create a binary search algorithm in Python. Make sure it is optimized for time complexity, and handle edge cases like empty lists.”
O1 Prompt:
“Write an optimized binary search algorithm in Python.”

Again, O1 factors in the edge cases without needing them to be explicitly stated.

5. Reversing a String

GPT-4o Prompt:
“Write a Python function to reverse a string. Ensure it handles Unicode characters and edge cases like empty strings.”
O1 Prompt:
“Write a function to reverse a string in Python.”

O1 includes handling for Unicode and edge cases, interpreting them as implicit requirements of the task.

6. Debugging Python Code

GPT-4o Prompt:
“I have a Python function that isn’t working as expected. It should return the sum of two numbers but throws an error. Can you debug it?”
O1 Prompt:
“Debug this Python function that adds two numbers.”

O1 doesn’t need a detailed description of the problem; it will assess the code, spot errors, and suggest fixes.

7. Writing Unit Tests

GPT-4o Prompt:
“Write unit tests for a Python function that adds two numbers. Make sure to include both valid and invalid input cases.”
O1 Prompt:
“Write unit tests for a Python function that adds two numbers.”

O1’s reasoning abilities ensure that both valid and invalid test cases are included without being explicitly asked.

8. Implementing Quicksort

GPT-4o Prompt:
“Create a quicksort algorithm in Python. Ensure it is optimized and handles large datasets efficiently.”
O1 Prompt:
“Write a quicksort algorithm.”

O1 interprets the prompt and produces an efficient, optimized version of quicksort suitable for large datasets.

9. File Operations in Python

GPT-4o Prompt:
“Write a Python script to read from a text file, process the lines to remove empty spaces, and write the output to a new file. Include comments explaining each step.”
O1 Prompt:
“Write a Python script to read from a file and write processed lines to another file.”

O1 automatically handles text processing, removing empty spaces without needing further clarification.

10. Handling JSON in Python

GPT-4o Prompt:
“Write a Python function to parse a JSON object, extract specific fields, and return them in a dictionary. Ensure input validation and provide comments.”
O1 Prompt:
“Write a Python function to parse a JSON object.”

O1 includes input validation and proper data extraction without needing additional instructions.

Why Simpler Prompts Work Better with O1

The fundamental difference between GPT-4o and O1 is how they handle reasoning. GPT-4o relies on detailed prompts to ensure accuracy and manage complex scenarios. In contrast, O1 uses its built-in reasoning tokens to understand the task more thoroughly. This allows developers to focus on the end goal instead of specifying every step, saving time and reducing complexity.

When to Use GPT-4o vs. O1

GPT-4o is useful when you need to fine-tune the AI’s behavior for very specific or niche tasks. Its ability to handle more complex instructions makes it ideal for tasks that require detailed guidance.
O1 is perfect for general-purpose code generation where you can trust the model to handle the details. Its simpler prompting mechanism makes it the better choice for quick, efficient coding tasks.

Conclusion

As AI continues to evolve, models like O1 are pushing the boundaries of code generation by simplifying the interaction between humans and machines. By using concise, direct prompts, you can harness the full power of O1's reasoning capabilities, making your workflow more efficient and your code more optimized.

In summary, knowing when to use simpler prompts with O1 versus more detailed instructions with GPT-4o can greatly improve your approach to coding tasks with AI. Whether you're debugging, optimizing, or generating new code, O1 allows for a streamlined, less hands-on interaction—saving time and reducing the cognitive load on the user.

GPT-4o vs. O1: Simplifying Prompts for Code Generation