What is SQL AI Training?
SQL AI Training is a specialized feature within Athenic AI that allows users to enhance the platform's query translation capabilities by providing explicit examples of how natural language questions can be converted into SQL queries. This feature is particularly useful when dealing with complex queries or when the AI needs guidance to understand the specific ways in which a company structures its data queries. By offering concrete SQL examples, users can directly influence how Athenic AI interprets and processes natural language requests, leading to more accurate and efficient SQL query generation. This training is akin to teaching the AI the specific dialect of SQL that your company speaks, ensuring that the AI's translations are in line with your data retrieval and analysis practices.
How does SQL AI Training work?
SQL AI Training is essential for tailoring Athenic AI's capabilities to the unique data landscape of each company. Given that every organization has its own set of metrics, calculations, and business logic embedded within its data, providing the AI with example SQL queries allows it to grasp these nuances. When the AI is trained with these examples, it gains a deeper understanding of how to navigate and interpret the company's data, leading to more precise and contextually relevant answers to user queries. This training process effectively equips the AI with a more sophisticated map of your data's terrain, enabling it to generate SQL queries that accurately reflect your company's specific analytical needs and preferences.
How do you use SQL AI Training?
To engage with SQL AI Training, users can navigate to the “AI Training” section within any project on the Athenic AI platform and select the SQL Training tab. Here, users can input their questions in natural language and then provide the corresponding SQL query that they would typically use to retrieve the desired information. This dual-input approach allows the AI to learn directly from the user's expertise, creating a bridge between natural language and the precise SQL commands that are necessary to extract the correct data.
Examples of SQL AI Training
For instance, if a company has a proprietary method for calculating Customer Lifetime Value (CLV), it is crucial for the AI to understand this specific formula. By providing an example SQL query that accurately computes the CLV for each customer, the AI is trained to apply this calculation consistently across all relevant queries. This means that whenever a user asks a question about customer lifetime value, the AI will utilize the trained SQL pattern to generate the correct query. This example illustrates how SQL AI Training can be used to ensure that the AI's responses are not only technically correct but also aligned with the company's unique analytical methodologies and business insights.