gpt-4o vs gpt-4o-mini: VisionGPT Showdown and CBB Updates

In this section, we'll take a deep dive into the performance comparison between the GPT-4 Mini and the GPT-4 models for image analysis tasks.

We conducted a series of tests using various images, and the results were quite interesting. While the GPT-4 Mini performed admirably, providing detailed and accurate descriptions of the images, the GPT-4 model seemed to offer a slight edge in terms of additional details and nuance in its analysis.

The key findings from our tests are:

• The GPT-4 Mini produced concise and relevant descriptions of the images, capturing the main elements and overall scene effectively.
• The GPT-4 model, on the other hand, tended to provide slightly more elaborate and nuanced descriptions, highlighting additional details and subtleties in the images.
• The difference in output length between the two models was not significant, suggesting that the GPT-4 Mini's analysis is just as comprehensive as the more expensive GPT-4.
• However, for users who require the absolute best in image analysis capabilities, the GPT-4 model may be the preferred choice, despite its higher cost.

Ultimately, the decision between the GPT-4 Mini and the GPT-4 will depend on the specific needs and budget of the user. Both models have demonstrated impressive image analysis capabilities, and the choice will come down to balancing performance and cost considerations.

We have updated our platform to allow users to choose between the GPT-4 Mini and GPT-4 models for image analysis. Previously, we defaulted to the GPT-4 Mini model as it was more cost-effective, but we have now given users the ability to select the model they prefer.

The new image analysis feature works as follows:

1. The user is prompted to upload an image.
2. Once the image is uploaded, the user can choose to analyze it using either the GPT-4 Mini or the GPT-4 model.
3. The selected model will then process the image and provide a text-based description of its contents.
4. The analysis results are displayed to the user, with the model used clearly indicated (e.g., "GPT-4 Mini analysis" or "GPT-4 analysis").

This update gives our users more flexibility and control over the image analysis process, allowing them to choose the model that best suits their needs and preferences. Whether you prefer the cost-effectiveness of the Mini model or the enhanced capabilities of the full GPT-4, our platform now has you covered.

We hope this new feature will help you get even more value out of our chatbot building platform. As always, let us know if you have any feedback or suggestions for further improvements.

In this section, we have added the ability for users to choose between the GPT-4 Mini and GPT-4 models when analyzing images. Previously, the platform would automatically use the GPT-4 Mini model, but now users have the flexibility to select the model they prefer.

The key steps are:

1. We added a new Open AI action block to the flow, allowing users to choose between the GPT-4 Mini and GPT-4 models.
2. We set the default model to GPT-4 Mini, but gave users the option to switch to the GPT-4 model if they desired.
3. We saved the selected model's output to separate custom fields, so the results from each model could be displayed to the user.
4. We included static text to clearly indicate which model was used for each analysis, making it easy for users to compare the results.

This update provides users with more control over the language model used, allowing them to choose the one that best fits their needs. It also helps to ensure that the platform is offering the latest and most capable AI tools for image analysis.

The key points in this section are:

1. The initial voting results were skewed due to a mistake in the setup, where both options were directing votes to the "mini" option.

2. After correcting the issue, the voting results showed that the majority of users preferred the "mini" language model over the more expensive "GPT-40" option.

3. The platform provides robust tracking capabilities, allowing the admin to see the unique and total votes for each option. This data is logged as custom events, which can be further analyzed or integrated into other systems.

4. The admin was able to quickly identify and fix the issue with the voting setup, demonstrating the platform's flexibility and the team's responsiveness to user feedback.

5. The admin emphasized the importance of providing examples in prompts, as it helps the language model understand the desired output and maintain the conversation flow.

6. Overall, the section highlights the platform's ability to gather user feedback, track analytics, and quickly iterate on the chatbot experience - all while maintaining transparency and a focus on user needs.

The assistant should provide 2-3 dynamically suggested topics that the user can choose from to continue the conversation after the initial question has been answered. This allows the conversation to flow naturally and gives the user options to steer the discussion in a direction they are interested in.

For example, after answering the initial question about Chick-fil-A's HR policies, the assistant could suggest the following additional topics:

1. Chick-fil-A's employee training and development programs
2. Opportunities for career advancement within Chick-fil-A
3. Chick-fil-A's approach to work-life balance for their employees

By providing these relevant and engaging follow-up topics, the assistant can keep the user interested and encourage a more dynamic, back-and-forth conversation.

To generate product recommendations with images, we'll follow these steps:

1. Create a table in a Google Sheet with the following columns: Product Name, Description, and Price (without currency symbol).
2. Import the product data from the Google Sheet into Chatbot Builder AI.
3. Create a flow that allows the user to request to see either software or apparel products.
4. Use the show software products and show apparel products functions to display the relevant product recommendations.
5. Include the product image URLs in the recommendations to display the product images.

The key aspects of this implementation are:

• Leveraging the e-commerce integration in Chatbot Builder AI to easily import product data.
• Creating functions to handle the user's requests for specific product categories.
• Displaying the product recommendations with the associated image URLs.

This approach allows you to provide a seamless product recommendation experience within your chatbot, making it easy for users to browse and discover relevant products.

The key takeaways from this session are:

1. CBB Nation is constantly updating and improving their platform to provide the best possible experience for their users. They recently added the ability to choose between GPT-4 Mini and GPT-4 models for image analysis, giving users more flexibility.

2. The team at CBB Nation values feedback from their co-creators (customers) and is quick to implement requested features and fixes. They strive to be transparent and accountable to their community.

3. When it comes to comparing the performance of GPT-4 Mini and GPT-4 models, the results were quite similar in this test case. The team recommends considering the cost difference and prompting skills when deciding which model to use.

4. CBB Nation has a strong focus on providing excellent customer support and building trust with their community. They shared a touching story about supporting one of their long-time customers during a difficult time.

5. The platform offers a range of powerful features, including the ability to generate images, log custom events, and create dynamic conversations with suggested topics. The team is committed to continually expanding the capabilities of their chatbot builder.

Overall, this session highlighted CBB Nation's dedication to innovation, user feedback, and delivering a top-notch chatbot building experience for their community.