Gen AI Best Practices
ONE provides your with a set of powerful Generative AI features to automate and enhance your data quality and management tasks.
Read through this article before using Generative AI in ONE as it sets the foundation for exploring Generative AI features of ONE.
It gives you a brief overview of these AI tools and includes practical examples and tips for proper prompt engineering, ensuring a smooth experience as you unlock the potential of AI in your workflows.
Gen AI features in ONE
ONE offers two main options for incorporating Gen AI:
- Embedded features
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Tools integrated directly into the platform, such as text-to-SQL, text-to-DQ-rule, and generating descriptions.
- AI Agent
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A powerful assistant capable of handling both atomic tasks and complex multi-step workflows.
Key embedded Gen AI features
The following are some of the key embedded Generative AI capabilities available in the current version:
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Text-to-SQL/SQL-to-Text - Write plain-language prompts to generate SQL queries.
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Example: “Generate a query to select customer names and emails where the credit limit exceeds $100,000, ordered by credit limit.”
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Output:
SELECT customername, email FROM customers WHERE creditlimit > 100000 ORDER BY creditlimit DESC;.
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Text-to-DQ Rule - Create and debug data quality rules from natural language descriptions.
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Example: “Create a DQ rule to validate email addresses.”
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Enhanced Descriptions - Automatically generate or improve descriptions for catalog items, terms, and attributes.
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Example: "Generate a description for the column 'customer_id' in the 'orders' table."
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Explain ONE Expression - Simplify complex expressions by converting them to plain English.
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UI Translation and Text Tools: Localize user interfaces and refine text clarity.
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Recommended resources
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Tips for prompt engineering
It’s common to underestimate how much guidance AI needs to provide useful and accurate answers. To get the most out of Gen AI, consider the following best practices for crafting prompts you could submit to Gen AI features like Data Quality Rule Generation, Chat with Documentation, or Text-to-SQL:
- Treat the AI like a human intern
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When you give the AI a prompt, ask yourself whether a human intern would be able to perform the task with the same information. In other words, treat the AI like someone who might need extra context to be able to accurately perform the task at hand.
- Be specific
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Be specific about the goal of your prompt and make sure to define the details in the prompt clearly. Let’s say you want to create a DQ rule to “find data records in Q2”. It might be helpful to define what “Q2” means. For example: ”Find data records that were logged in April, May, or June.”
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Ineffective prompt example: “Tell me about the data.”
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Effective prompt example: “List all tables containing customer information in the catalog.”
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- Use context
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Add context to the prompt to make sure you fill in any missing potential details.
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Ineffective prompt example: “Find null values.”
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Effective prompt example: “Find columns with more than 50% null values in the sales dataset.”
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- Provide examples in the prompt
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When asking for rules or descriptions, try to include sample data or expected formats.
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Ineffective: “Check for SSNs.”
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Effective: “Check for US Social Security numbers that have the format of 123-45-6789.”
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- Iterate
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Start with a general prompt and refine based on the output. For example:
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Initial prompt: “Generate a DQ rule for emails.”
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Iteration: “The rule should validate that emails include a domain and an '@' symbol.”
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FAQ
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