Prompt Engineering – Bridging the Gap between Natural Language and SQL

While LLMs like GPT-3 are powerful at generating human-like text, they are not inherently aware of the specifics of your database schema or the nuances of SQL queries. This is where prompt engineering comes into play. It involves crafting a well-structured and informative prompt or query for the model so it can generate SQL commands that accurately reflect the user’s intent and the database structure.

Also read: Creating a Natural Language to SQL Application with OpenAI’s GPT-3 and Its Applications Across Industries

To make the LLM understand the database structure, the prompt should provide contextual information about the database schema. You can include descriptions of tables, columns, and relationships. For example:

Database Schema:

• Table: Customers
  • Columns: CustomerID, FirstName, LastName, Email
• Table: Orders
  • Columns: OrderID, CustomerID, OrderDate, TotalAmount

Clearly convey the user’s intent in the prompt. This helps the model generate SQL queries that align with what the user wants to achieve. For instance:

User Query: “Retrieve the names and email addresses of all customers who made a purchase in the last month.”

Provide an example of the expected SQL query structure within the prompt. This can help guide the model in generating SQL commands that are syntactically correct. For example:

SQL Query: “SELECT FirstName, LastName, Email FROM Customers WHERE CustomerID IN (SELECT CustomerID FROM Orders WHERE OrderDate >= ‘2023-08-01’)”

The more context and specificity you provide in the prompt, the better the LLM can understand the query and the database structure. It reduces the likelihood of the model generating incorrect or overly complex SQL statements.

Also read: Generative AI: A Journey Beyond the Hype Cycle

Here’s an example of a well-engineered prompt that incorporates the elements mentioned above:

Prompt: “Given the following database schema, write an SQL query to retrieve the names and email addresses of all customers who made a purchase in the last month:

• Table: Customers
  • Columns: CustomerID, FirstName, LastName, Email
• Table: Orders
  • Columns: OrderID, CustomerID, OrderDate, TotalAmount”

With this carefully crafted prompt, the LLM can better understand the user’s query, interpret the database structure, and generate an accurate SQL command.