OpenAI o1 mind blowing Test
You've probably heard of O1 and read numerous articles praising its capabilities. One of the most famous examples highlighting its prowess is its ability to accurately count the number of "r"s in the word "strateberry," a task where many other large language models (LLMs) falter. While impressive, this example isn't a typical use case and doesn't fully showcase why O1 stands out among its competitors.
In this post, we'll explore a more practical use case that combines** multiple skills, **demonstrating the true power of O1---something that only a powerful LLM can handle effectively.
A Practical Use Case: Finding the Best Freelancer for Your Marketing Needs
Imagine you're on a freelancer platform searching for a marketer. The platform hosts countless great freelancers, but how do you identify the best fit? Typically, platforms use random sorting mechanisms that may not align with your specific search criteria. This is where an LLM like O1 can revolutionize your search by handling the cognitive load involved in filtering and analyzing data to find the perfect match.
The Traditional Approach
Usually, you'd have to:
- Manually sift through freelancer profiles.
- Evaluate each freelancer based on your criteria, such as the number of reviews, ratings, and specific skills.
- Compile and compare the data to make an informed decision.
This process is time-consuming and mentally taxing. Instead, let's leverage an LLM to automate this task efficiently.
Combining Multiple Skills
This test case combines many skills, including:
Demonstrating LLM Capabilities
To illustrate, I used a prompt designed to filter freelancers with more than 200 reviews from a sample dataset. Here's the prompt I employed:
You will be analyzing data about freelancers and creating a summary table. Your task is to filter the data for freelancers with more than 200 reviews and present the information in a structured format. Follow these steps:
1. First, carefully read through the provided freelancer data:
2. Filter the data to include only freelancers with more than 200 reviews.
3. Extract the following information for each qualifying freelancer:
- Name
- Service
- Number of reviews
4. Count the total number of freelancers that meet the criteria.
5. Create a table with the following columns:
- Name
- Service
- Number of Reviews
6. Populate the table with the information extracted in step 3.
7. Provide your answer in the following format:
<answer>
Total number of freelancers (with >200 reviews): [Insert total count here]
[Insert the table here, using markdown format for better readability]
| Name | Service | Number of Reviews |
|------|---------|-------------------|
| [Freelancer 1 Name] | [Service 1] | [Number of Reviews 1] |
| [Freelancer 2 Name] | [Service 2] | [Number of Reviews 2] |
...
</answer>
Make sure to include all qualifying freelancers (>200 reviews) from the provided data in your table, sorted alphabetically by name.
If the provided data is empty, does not contain any freelancer information, or no freelancers meet the criteria of having more than 200 reviews, respond with:
<answer>
No qualifying freelancer data available.
</answer>
Remember to only include freelancers with more than 200 reviews in your analysis and table.
<< COPY/PASTE THE WEBPAGE CONTENT HERE >>
This prompt instructs the LLM to filter freelancers based on the number of reviews and present the results in a structured table. To ensure fairness, I tested this prompt across various LLMs, including GPT-4, Claude, Gemini, and, most notably, OpenAI's O1.