Act as a SQL terminal
Act as a SQL terminal
I want you to act as a SQL terminal in front of an example database. The database contains tables named "Products", "Users", "Orders" and "Suppliers". I will type queries and you will reply with what the terminal would show. I want you to reply with a table of query results in a single code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so in curly braces {like this). My first command is 'SELECT TOP 10 * FROM Products ORDER BY Id DESC'
1. Prompt Analysis: Main Aspects, Strengths, and Weaknesses
Main Aspects:
- SQL Terminal Role: The AI is instructed to act as a SQL terminal, emulating SQL command responses as though connected to a database.
- Example Database: The database consists of four tables —
Products
,Users
,Orders
, andSuppliers
. - User Commands: The user will issue SQL queries, and the AI is required to return responses without additional commentary or explanation.
- Response Format: Responses are requested in table format, rendered within a single code block.
- Command Syntax: The first query provided by the user demonstrates SQL syntax for selecting the top 10 rows from the
Products
table, ordered by descendingId
. - Natural Language Instructions: The user specifies that any natural language communication will be enclosed in curly braces
{}
, creating a clear distinction between commands and conversation.
Strengths:
- Clear Role Specification: The prompt explicitly defines the AI’s role as a SQL terminal, ensuring the chatbot understands the format and structure of expected responses.
- Defined Output Format: The request for responses in a single code block with no explanation minimizes confusion and ensures the chatbot focuses strictly on data output.
- Example Query: By providing a sample SQL query (
SELECT TOP 10 * FROM Products ORDER BY Id DESC
), the user clarifies their expectations for the chatbot's behavior in response to SQL commands. - Separation of SQL and Natural Language: The use of curly braces for natural language instructions provides a structured way to manage mixed content queries, reducing ambiguity.
Weaknesses:
- Lack of Detailed Context: While the prompt defines four tables, it doesn't provide any schema information, which could lead to confusion about the table structures and field names in future queries.
- Assumption of Terminal Behavior: The user assumes that the chatbot can perfectly mimic a SQL terminal, but this could be challenging without specifics about database constraints, joins, or schema relationships.
- Limited Query Complexity: The example query is simple and doesn't address more complex SQL features like joins, subqueries, or functions, potentially limiting the chatbot's effectiveness in handling advanced scenarios.
- Database Population: There's no mention of the volume of data or what happens when queries exceed limits or require paging, which may be a challenge if responses grow too large.
2. Suggested Improvements and Enhanced Prompt Example
Enhancements:
- Schema Information: Providing basic schema details about each table would help the AI respond accurately to future queries involving column names, data types, and relationships.
- Clarification of Error Handling: Adding instructions on how to handle cases where a query is invalid or where there are no results would make the prompt more robust.
- Complex Queries: Including examples of more complex SQL queries involving
JOIN
,WHERE
, orGROUP BY
clauses would prepare the chatbot for more advanced database interactions. - Data Limitations: Specifying what the AI should do in case the output exceeds a certain size would ensure manageable outputs in scenarios with larger datasets.
Improved Prompt:
I want you to act as a SQL terminal connected to a database with the following tables: "Products", "Users", "Orders", and "Suppliers". Each table's schema is:
- Products: (Id INT, Name VARCHAR(100), Price DECIMAL, SupplierId INT)
- Users: (Id INT, Name VARCHAR(100), Email VARCHAR(100), CreatedAt DATETIME)
- Orders: (Id INT, UserId INT, ProductId INT, Quantity INT, OrderDate DATETIME)
- Suppliers: (Id INT, Name VARCHAR(100), ContactInfo VARCHAR(255))
I will type SQL queries, and you will return results as tables in a single code block without explanations. If there are no results or the query is invalid, reply with "No results" or "Invalid query" instead of throwing an error. Use the first 10 rows if the result set is too large. Natural language instructions will be given in curly braces
{}
.My first command is
SELECT TOP 10 * FROM Products ORDER BY Id DESC
.
3. Continuing the Conversation for Better Results
Once the user starts issuing SQL queries, they can further improve their interaction with the chatbot by:
- Requesting Specific Columns: Instead of selecting all (
*
), the user could refine queries to fetch specific columns for more readable results. For example,SELECT Name, Price FROM Products ORDER BY Price DESC
. - Joining Tables: To simulate a more realistic database interaction, the user could issue commands that join multiple tables. Example:
SELECT Users.Name, Orders.Quantity, Products.Name FROM Orders JOIN Users ON Orders.UserId = Users.Id JOIN Products ON Orders.ProductId = Products.Id
. - Filtering with WHERE: Introducing filtering conditions like
WHERE
clauses would allow the user to explore specific subsets of data. Example:SELECT * FROM Products WHERE Price > 100
. - Aggregating Data: Using
GROUP BY
orCOUNT()
would demonstrate the ability to perform more advanced analysis. Example:SELECT COUNT(*) FROM Orders WHERE OrderDate > '2023-01-01'
.