It is in the early stages. Currently, the model has the key documentation loaded (see below). I haven’t yet started the configuration or testing.
The ODM is robust, but that robustness creates challenges for our easy-to-use paradigm. I expect LLMs developed for ODM will help.
Documents provided to develop the GPT:
Documentation pdf (v2.1.0).
MD and qmd files from the following GitHub repositories. I indicated work in progress for the last three.
- main repo
- docs
- validate
- sharing
- mapping
the ERD (pdf)
the excel dictionary and data input templates.
the main website URL was provided.
I haven’t yet added:
OADP
SQL definitions
Test data
Additional info about wide names, creating IDs, other dictionaries (i.e PHA4GE).
There are still steps to configure, develop, and test the model. However, the first responses to the questions look good and promising.
Below are the next development steps, as described by ChatGPT.
GPT Builder
The GPT, named “PHES ODM Comprehensive Guide,” has been updated with a focus on providing AI-driven insights into using the Public Health Environmental Surveillance Open Data Model (PHES ODM). It’s designed to assist users in understanding and navigating the complex data model specific to public health and environmental surveillance.
The next steps in refining this GPT could include:
Enhancing Domain Knowledge: Incorporating detailed and specific information about PHES ODM, focusing on its latest updates and features.
User Interaction Optimization: Developing guided interactions for users of varying expertise levels in data management and PHES ODM, including interactive query-response systems or step-by-step guidance.
Feedback Integration: Gathering and incorporating user feedback to tailor the GPT’s responses more effectively, based on common queries and difficulties users encounter.
Advanced Features Development: Exploring the integration of features such as data visualization tools or interactive ERD explorations for a more immersive understanding of the data model.
Customization for Specific Use-Cases: Tailoring responses to specific public health and environmental surveillance scenarios, making the GPT more relevant and practical.
Model Configuration: Setting up model parameters and capabilities to align with users’ needs, including defining the scope, depth, and format of responses.
Data Integration: Ensuring the model has access to and can interpret the latest and relevant data about PHES ODM, including integrating new data sources or updating existing ones.
Custom Response Design: Crafting responses that are informative, engaging, and easy to understand, including simplifying complex concepts and providing examples or analogies.
User Experience Enhancement: Focusing on improving user interaction with the model, making the experience more intuitive and helpful.
Performance Monitoring and Updating: Continuously monitoring the model’s performance to identify improvement areas, and updating the model to ensure accuracy and relevance.
User Testing and Feedback Incorporation: Conducting user testing sessions to gather feedback on the model’s usability and performance, and incorporating this feedback for iterative improvements.
These steps aim to create a comprehensive, user-friendly, and up-to-date guide for PHES ODM developers and users, enhancing their ability to navigate and utilize the Open Data Model effectively.
And just a reminder that GPTs will only be as good as the how well we document the model.
From a question about updating the documents.
From GPT Builder
Absolutely, updating the documents and versions of the ODM used in the GPT model is both possible and recommended, especially considering that the ODM is still in development. Continuous updating ensures that the GPT model remains aligned with the latest information and changes in the ODM. Here’s how this process typically works:
Regular Updates: As new versions of the ODM documents become available, you can upload them. This keeps the GPT model informed about the latest developments, changes, or additions in the ODM.
Model Re-training: Upon receiving new or updated documents, the GPT model can be re-trained or adjusted to incorporate these changes. This ensures that the model’s responses remain accurate and up-to-date with the latest ODM version.
Version Control: It’s important to manage the versions of the ODM documents effectively. Tracking changes between versions helps in understanding how the ODM evolves over time and ensures that the GPT model can reference specific versions accurately when needed.
Feedback Loop: As the ODM develops, feedback from users interacting with the GPT model can be invaluable. This feedback can identify areas where the model needs refinement to better handle the evolving ODM.
Periodic Reviews: Regularly scheduled reviews of the GPT model’s performance, especially after incorporating updates to the ODM, help ensure continued alignment and accuracy.
Documentation and Communication: Keeping clear documentation of the updates and changes made to the GPT model in response to ODM updates is crucial. This helps users understand the capabilities and limitations of the GPT model at any given time.
Remember, the goal is to maintain a dynamic and responsive model that grows and adapts alongside the ODM.