Generated Knowledge prompting in LLMs

Large Language Models (LLMs) like ChatGPT are powerful tools that can generate responses to a wide range of prompts. However, the quality of their output depends heavily on how you structure the prompt. One effective method to improve these responses is Generated Knowledge Prompting.

 

1. What is Generated Knowledge Prompting?

Generated Knowledge Prompting involves providing additional data or context to the LLM along with your actual query. This extra knowledge is related to your specific use case, helps the model understand the task better and produce more relevant results.

 

Think of it as creating a cheat sheet for the LLM. By feeding it structured information related to your domain (e.g., a list of products or company details), you set the stage for more accurate responses.

 

2. Example: SQL Query Generation Example for Generated Knowledge Prompting

 

2.1 Scenario

You have a database with multiple tables, and a user asks a question like:
"Show me the names of employees and their department from the HR database."

 

To generate a SQL query for this, you provide context about the database (e.g., table names and structures) along with the user's query.

 

2.2 Define a Template

 Create a prompt template with placeholders.

 

Database: {{database_name}}  
Tables:  
{{tables_list}}  

Please generate a SQL query based on the following request:  
{{user_query}}  

Return the output in JSON format: {"query": "ACTUAL_QUERY"}

2. 3 Fill the Template with Context

Add real data to the placeholders.

Database: HR  
Tables:  
- employees (id, name, age, department_id)  
- departments (id, name)  

Please generate a SQL query based on the following request:  
"Show me the names of employees and their department."  

Return the output in JSON format: {"query": "ACTUAL_QUERY"}

Step 3: LLM Generates the Output

With this context, the LLM generates.

 

{
  "query": "SELECT employees.name, departments.name AS department FROM employees JOIN departments ON employees.department_id = departments.id;"
}

How This Example Fits Generated Knowledge Prompting

1.   Augments Knowledge: The LLM lacks direct access to your database schema. By providing the table and column details, you fill in the knowledge gaps.

2.   Guides Query Construction: Structured data about the schema guides the model to form correct SQL syntax.

3.   Natural Language Understanding: The user's query ("Show me the names of employees and their department") is converted into an actionable SQL statement.

 

When It’s Useful

·       For tools that allow non-technical users to query databases using natural language.

·       Automating database interactions in chatbots or analytics platforms.

·       Reducing the learning curve for SQL by enabling users to describe their needs in plain language.

 

Pros and Cons of Using Generated Knowledge Prompting for SQL Query Generation

Pros

1.   Accuracy: Reduces errors by guiding the LLM with schema details.

2.   Ease of Use: Simplifies database interactions for non-SQL users.

3.   Customizability: Works for any database as long as the schema is provided.

4.   JSON Format: Makes it easier to integrate with APIs and applications.

Cons

1.   Data Dependency: The accuracy depends on providing up-to-date schema details.

2.   Complex Queries: For intricate queries with subqueries or advanced joins, prompt engineering becomes challenging.

3.   Security Risks: Care must be taken to avoid exposing sensitive schema information to unintended users.

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