Customize AI-powered Applications with your Own Data

Step-by-Step Tutorial: Customizing AI-powered Applications with Your Own Data

1. Understanding Data Sources in MindStudio:

   • Data sources in MindStudio allow you to integrate external data into your applications.
   • Data sources can be used to save costs on tokens, explore datasets, and upload large databases.

2. Differentiating Between Query Data Source and Fine-Tuning:

   • Fine-tuning involves creating a custom model by training the AI with examples and outputs.
   • Query data source, on the other hand, retrieves vectors from datasets and is used to query information.

3. Utilizing Vectorization in MindStudio:

   • Vectorization in MindStudio involves converting data into vectors that can be recalled from query data sources.
   • You can upload CSV or Excel files into vectors for querying.

4. Creating a Query Data Source Workflow:

   • Start by adding a new data file such as a CSV, XLSX, DOCX, or HTML file.
   • Define the list of sources, purpose, and description for future reference.

5. Building a Workflow with Query Data Sources:

   • Create a workflow that asks the user for a query, queries multiple data sources, and sends a response message.
   • Use the extracted data from query data sources to provide accurate responses.

6. Handling User Interactions and Feedback:

   • Incorporate a menu to allow users to provide feedback or end the conversation.
   • Use the menu to guide users through different options based on their satisfaction with the response.

7. Refining Queries for Improved Results:

   • Use a Send Message block to refine user queries before sending them to query data sources.
   • Refining queries ensures that the AI receives clear and relevant inputs for better results.

8. Utilizing Revised Document Feature:

   • Use the Revised Document feature to edit and refine text outputs from AI responses.
   • Select text in the editor, apply instructions for AI refinement, and generate revised content for improved accuracy.

9. Optimizing Token Usage and Context Window:

   • Summarize data to reduce token usage and fit within context window limits of AI models.
   • Test with different models to understand token usage and adjust accordingly for optimal performance.

10. Enhancing User Experience with AI Customization:

    • Incorporate AI features like summarization, refinement, and context-based responses to enhance user interactions.
    • Test and iterate workflows to ensure seamless integration of AI capabilities with user-generated content.

By following these steps, you can effectively customize AI-powered applications in MindStudio, leverage external data sources, and enhance user experiences with tailored responses and interactions.