AI Notes

Notes are natural language instructions for the AI agent.

Creating Notes

To create a note, click 'Add Note' and then type or paste in your note. Good notes are:

  • concise - to conserve tokens

  • specific - referencing exact names within the data such as a column name or value within a column

  • human understandable - a good sanity check for your note is, "Could an intern that hasn't worked with this data before understand this note?"

Notes can (and often should) be linked to multiple datasets. This allows you to centralize logic for explaining something that applies to multiple datasets in your account.

Types of Notes

Notes are classified by AI into 3 categories:

  1. Data Lineage - where the data comes from

  2. Business Context - explanations of nuances related to your business

  3. Semantic Layer - specific column and table relationships such as KPI definitions and join keys

As part of the AI Readiness evaluation, we check for coverage across each of these categories for each AI-visible dataset.

Example Notes

Here are some example scenarios where you may want to create a note:

Explaining an Acronym

Does your organization have a pressing need to abbreviate everything? You're not alone, but this can be confusing for AI. Use Notes to teach AI your acronyms, i.e.:

CACP means Customer Acquisition Cost Payback and is calculated as Sales & Marketing Expenses in Period / (Net New MRR Acquired in Period * Gross Margin)

Directing AI to Choose the Right Table

Do you have tables that look very similar for different business units? You can teach AI which table to use in which scenario with a note, i.e.:

The table prefix "ABC" refers to our alcohol business unit, and "DEF" refers to our diesel fuel business unit

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