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Hello Glide Team :waving_hand:

My name is Michael Jones. I’m developing a diagnostic and continuous improvement app for SMEs, based on my own SENTIO methodology.

The idea is for users to complete a diagnostic form within Glide, the information to be saved in Google Sheets, and then, using Make, to send that information to OpenAI to generate personalized automatic recommendations.

My goal is for:
Users to be able to see their risk “traffic light” (status) within the app.
They receive an AI-generated improvement plan, either within the app or by email.

:white_check_mark: What I already have:
Active Glide account ([Starter/Pro] plan).
Google Sheets with organized forms and fields.
Make.com account with basic integrations.
OpenAI API key ready.

My questions:
:one: What’s the best way to connect Glide with Make to send and receive AI-generated results?
:two: Can I display the text generated by OpenAI directly within the Glide app?
:three: Are there examples or templates for integrating AI into Glide using Make?
:four: Should I use Glide Tables or Google Sheets as the primary base in this case?
Any advice, resources, or templates would be a great help.

Thank you very much for your time and support. :raising_hands:

2

Will attempt best answers here:

  1. You can use the Glide Make.com integration, docs here
  2. My recommendation would be to use the Make integration to send the content/response from OpenAI and store it into a text/rich text/etc. column. You will likely have to work on some formatting within the Glide UI to accomplish your desired result. Alternatively, you could see if your OpenAI use case is covered by the integration provided by Glide.
  3. Numerous examples, I would spend some time searching this community, as well as Youtube tutorials from various experts.
  4. I personally find there are very few reasons to use Google Sheets as a storage mechanism in Glide these days. If you’re realistically expecting some amount of scale, Glide Big Tables are a good option, with some limitations. Glide tables are great for smaller use cases, but processing is done on the client’s device. There is a lot of documentation in this community and on the docs page to help you decide.
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