Hello, fellow Bubblers! 
We are excited to announce the launch of:
Fine-Tuner.ai, an easy-to-use AI optimization platform that allows you to fine-tune your machine learning models without writing a single line of code 
Fine-Tuner.ai plugin for Bubble 
Fine-Tuner.ai provides a simple drag-and-drop interface to prepare your training data, choose a base model to fine-tune, set model hyperparameters, and train optimized AI models tailored to your needs.
And with the plugin you can:
- Call your fine-tuned models directly from your Bubble workflows.
- Submit new data samples to the existing training dataset directly from Bubble.
- Fetch the training data associated with the current fine-tuned model.
Use Cases
- Customer Acquisition: Improve targeting and marketing campaign effectiveness. Increase conversion rates and growth.
- Predictive Analytics: Improve predictive analytics. Make data-driven decisions and gain business insights.
- Personalization: Personalize customer experiences. Provide a seamless and engaging experience. Improve satisfaction.
- Automation: Automate routine tasks. Free up time and resources to focus on other important tasks.
- Financial Planning and Forecasting: Improve planning and forecasting. Make better decisions about investments, resources and future growth.
- Customer Service: Provide fast and accurate customer service. Improve satisfaction and reduce support team workload.
Examples:
- Customer churn prediction: Identify potential churn and implement targeted retention strategies.
- Product recommendation: Provide personalized recommendations to enhance UX and increase sales.
- Image classification: Automatically categorize images based on content. Enable improved organization and search.
- Sentiment analysis: Analyze text to determine sentiment. Improve customer service and understand user feedback.
- Anomaly detection: Detect unusual patterns in data. Identify potential issues or opportunities.
Features:
- No-code AI model creation: Easily build and optimize models without coding
- Pre-trained AI models: Choose from pre-trained models for computer vision, NLP, predictions, recommendations and more (coming soon)
- Optimized for your needs: Fine-tune models by training them on your data to tailor them to your use case
- Simple drag-and-drop interface: Intuitive workflow to prepare data, select a model, set hyperparameters and train an optimized AI model
- Integrate models into Bubble: Call your trained models directly from Bubble using the plugin
- Community support: Get help from the Stack3 community if you have questions on using the plugin or training AI models
- Always improving: We continuously release updates to the plugin and our AI models to provide the latest capabilities and best performance
- Free to use: The plugin is freely available to all Bubble users to empower them with AI at no cost
Check out the plugin here:
API docs:
https://fine-tuner.ai/api_ref
Stack3 community:
Fine-Tuner.ai makes AI more accessible than ever before. Sign up for free at fine-tuner.ai to get started optimizing your AI today!
Let us know if you have any questions or need help getting started. We’d be happy to assist you!
can be used on larger databases and complex schemas 
"Building Fine-Tuned Chatbots with No-code" tutorial on YouTube 
Slide deck of the tutorial
% for free!
This is really incredible. I’m very intrigued by the ability to create my own models programmatically via bubble and then supplement them train them on a custom knowledge base. That said, in looking over your documentation, I had a couple (probably ignorant) questions: If using the OpenAI base models, how would one specify a system message or provide context when interacting? By way of context, I’d like to be able to provide an initial system prompt to the chatbot to frame a conversation with an end user, but it’s not clear to me how to do that.