Retrieval-Augmented Generation (RAG) - with Oracle Database

What is RAG?

Retrieval-Augmented Generation (RAG) is a technique that combines:

• Retrieval: Fetching relevant, up-to-date information from a data source (like a database, documents, or search engine).
• Generation: Using a language model (LLM) to process that information and generate a response, summary, or decision.

How RAG is Used in Your Workflow (For example: A customer table in Oracle database)

• The script retrieves live customer data from Oracle (retrieval).
• It builds a prompt with this data and sends it to Gemini (generation) in my example.
• Here Gemini generates a summary or decision based on the actual, current data

What if You Don’t Use RAG?

If you use only a language model (LLM) without retrieval:

• The LLM relies solely on its training data, which may be outdated or generic.
• It cannot access your real-time or private data (like your Oracle customers).
• Answers may be less accurate, less relevant, or even hallucinated.

Example:

-- SQL script to create a sample 'customers' table for RAG demo

CREATE TABLE customers (

    customer_id NUMBER GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,

    customer_name VARCHAR2(100),

    account_status VARCHAR2(20),

    balance NUMBER(12,2)

);

-- Insert sample data

INSERT INTO customers (customer_name, account_status, balance) VALUES ('Alice', 'Active', 1200.50);

INSERT INTO customers (customer_name, account_status, balance) VALUES ('Bob', 'Inactive', 0.00);

INSERT INTO customers (customer_name, account_status, balance) VALUES ('Charlie', 'Active', 500.00);

INSERT INTO customers (customer_name, account_status, balance) VALUES ('Diana', 'Overdue', -50.00);

INSERT INTO customers (customer_name, account_status, balance) VALUES ('Eve', 'Active', 3000.00);

COMMIT;

• Without RAG:

       “Which customers need attention?”
       → The LLM guesses based on general knowledge, not your actual data.

• With RAG:

      “Which customers need attention?” (with your real customer data provided) → The LLM             analyses your actual data and gives a specific, actionable answer.

Key Differences

Aspect	With RAG	Without RAG
Data Freshness	Uses live, up-to-date data	Relies on old training data
Personalization	Answers are specific to your data	Answers are generic
Accuracy	High, as it uses real context	Lower, may hallucinate
Use Cases	Data-driven insights, automation, search	General Q&A, brainstorming

Here is the sample result, 

Prompt sent to Gemini: (use python script for RAG demo , where you can connect database and interact using   Gemini API)

Given the following customer data, summarize which customers need attention: Customer: Alice, Status: Active, Balance: 1200.5

Customer: Bob, Status: Inactive, Balance: 0.0
Customer: Charlie, Status: Active, Balance: 500.0
Customer: Diana, Status: Overdue, Balance: -50.0

Customer: Eve, Status: Active, Balance: 3000.0

Gemini API Output:

Based on the provided data, here's a summary of customers who likely need attention:

*   **Bob:** Inactive status and zero balance likely indicates churn. Should investigate why he's inactive and attempt to re-engage.

*   **Diana:** Overdue status and negative balance is a clear priority. Needs immediate attention to resolve the overdue payment.

Current Customers:

ID: 1, Name: Alice, Status: Active, Balance: 1200.5
ID: 2, Name: Bob, Status: Inactive, Balance: 0.0
ID: 3, Name: Charlie, Status: Active, Balance: 500.0
ID: 4, Name: Diana, Status: Overdue, Balance: -50.0

ID: 5, Name: Eve, Status: Active, Balance: 3000.0