U-M Maizey In Depth


Understanding how to use U-M Maizey proficiently will provide you with the most insight into your data. Refer to the tips below for guidance on how to use U-M Maizey.

Understand Prompts

Understanding how to write prompts and queries effectively will help you get the best from U-M Maizey. Refer to the following to learn more about prompts:

Source Data

Known file types supported in U-M Maizey include: .txt, .html, .rtf, .pdf, .docx, .xlsxl, .pptx, .md. Other file types may work, but have not been tested.

U-M Maizey works best with unstructured, natural-language text (words, phrases, sentences). The more documents indexed, the better the U-M Maizey data set becomes.

Best Practices with Source Data in U-M Maizey:

  • Use several small documents in your datasets rather than one large document.
  • Download and re-upload any Google Docs over four pages of text to DOCX before indexing.
  • Structured content such as spreadsheets will not index well.
  • Consider splitting your data into a collection of contextual text files and indexing that way; again, natural language sentence structures work best.
  • Content comprised of non-sentence text, such as abbreviations and condensed phrases (as you may find in resumes and similar documents), may not index well.
  • Remove special formatting, graphics, etc., to improve results.
  • Documents with embedded structured data such as JSON should not be used.

Creating a Project

Note the following when creating a project in U-M Maizey:

  • Once you create your project, you cannot change the data source used.
  • After creating a project, you can update the Project Name, Billing Shortcode, or Project Path URL.
  • Tips for creating a project in U-M Maizey:
    • If you make a mistake, it is best to delete the project and start over.
    • Test the URL before sharing it with others in the MCommunity group.
    • MCommunity groups can have multiple owners; therefore, if you are an owner in the MCommunity group, you are also the owner of any project created with that MCommunity group.
    • Projects that you own will be listed under "Your Projects."
    • You have edit rights for all projects that you "own."
  • Indexing Data for your project:
    • There is a delay as data is being indexed.
    • To determine if indexing is complete, click Check Task Results.
    • If your dataset is updated, click Index Data to refresh the data in the project.

User Interface

What to know about the user interface:

  • The name of your project is not related to the "Title" of your project's AI chat tool. The title of the project cannot be changed. The title of the AI chat tool can be changed by editing the project.

System Prompt Augmentation

System Prompt Augmentation allows you to define and fine-tune how the system responds to user prompts. View System Prompt guidance and examples.


Temperature allows you to adjust the sensitivity and output-randomness of the model. A medium temperature is recommended for most use cases.

The higher the temperature is set, the more "creative" the response to a query (i.e., you're giving it permission to "think outside the box"). However, a high temperature might result in answers that are too unusual. Higher temperatures are generally better suited for use cases where creativity is valued (e.g., generating song lyrics, poetry, etc.)

The lower the temperature is set, the more "conservative" of a response the AI model might provide to a query (i.e., you want it to stick to safe answers with a high probability of being accurate). However, a low temperature might result in the AI model missing some correct answers because it is not specific enough.

Data Chunks

Think of chunks as individual pieces of a puzzle. Each chunk contains a small, specific piece of information from a larger database. Just as each puzzle piece contains part of the image, each chunk contains a part of the response to your prompt. Maizey combines these chunks, like assembling a puzzle, to give you a complete response.

Maizey's default setting is four chunks, which works well for most use cases. Increasing the number of chunks allows Maizey to access more indexed data, which may enhance the quality and variety of query responses. 


U-M Maizey is trained on your custom data set alone. As with any AI technology, it is important to fact-check to ensure the information is accurately presented.