Prompt Engineering for SQL – Bridging the Gap between Natural Language and SQL

Why Prompt Engineering Matters

While LLMs like GPT-3 have a significant ability to produce human-like content, they are not inherently knowledgeable about your database schema or the meaning of SQL commands. This is where prompt engineering for SQL comes into play. This is about creating a good prompt or question for the model so that it can generate SQL commands that accurately reflect both 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

Key Components of Prompt Engineering for SQL

1. Contextual Information: In order for the LLM to understand what the database structure looks like, the prompt needs to provide the model with some contextual knowledge related to the database schema. Information about the tables, columns, and relationships could be included. For example:Database Schema:
   • Table: Customers
   • Columns: CustomerID, FirstName, LastName, Email
   • Table: Orders
   • Columns: OrderID, CustomerID, OrderDate, TotalAmount
2. User Intent: 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.”
3. Sample Query Structure: Give 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 Role of Context and Specificity

Essentially, (similarly to any context), the more context and specificity you include in the prompt, the better the LLM can understand the query and database structure, and it reduces the chances for the model to generate inaccurate or overly complex SQL statements.

To know more about LLMs, read our blog on verticalization and practical applications

An Example of a Well-Engineered Prompt

Here’s an example of a well-engineered prompt with all of the elements previously listed included:

Prompt: Given the following database schema, write an SQL query to return the names and email address 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 well-structured prompt, the LLM can understand the user’s query, understand the database schema, and return an appropriate SQL statement.

The Power of Prompt Engineering for SQL

As the next step in making a Natural Language SQL application, prompt engineering is where the user’s natural language intent or request meets the relational database and the rules of SQL. It is also a step in making sure that the language model will understand what the user means and that the SQL generated is syntactically correct and semantically interesting. If you spend some time and effort crafting solid prompts, you provide your application with a usable tool that can be utilized in different industries.