Writing Prompts for 'ChatGPT Over Your Own Data'

Imagine having an AI assistant that doesn't just know what's in its training data, but can actually search through your company documents, personal notes, or proprietary database before answering. That's the power of RAG—Retrieval-Augmented Generation—and it's transforming how businesses and individuals use AI in 2024.

But here's what most people miss: prompting a RAG system requires a different approach than prompting standard ChatGPT. Today, you'll learn how to craft prompts that leverage this powerful architecture effectively.

What Is RAG, Really?

RAG stands for Retrieval-Augmented Generation. In plain English, it's a system where the AI first searches through your specific documents or data sources, retrieves relevant information, and then uses that information to generate its response.

Think of it as giving the AI a custom library before asking it questions. Instead of relying solely on what it learned during training (which has a knowledge cutoff), it pulls from your current, specific, proprietary information.

Common RAG applications include:

• Customer service bots that search your knowledge base

• Research assistants that query your document collection

• Internal company chatbots that access policies and procedures

• Personal AI that references your notes and files

Why RAG Prompting Is Different

When you prompt a standard AI, you're working with a closed system. The AI only knows what's in its training data. But with RAG, you're working with a two-stage process: retrieval first, then generation.

This changes everything about how you should prompt.

Standard Prompt: "What's our company's remote work policy?" Problem: The AI will make something up based on generic knowledge.

RAG-Aware Prompt: "Search our HR documents for our current remote work policy. Summarize the key points including eligibility requirements and the approval process." Better: This explicitly tells the system to retrieve documents first, then guides what to extract from them.

Key Principles for RAG-Aware Prompting

Be Explicit About What to Search

Help the retrieval system find the right documents by being specific about what you're looking for:

Weak: "Tell me about the project." Strong: "Search our project documentation for the Q4 marketing campaign timeline and budget allocation."

The second prompt gives the retrieval system clear search terms that will surface relevant documents.

Reference Document Types or Sources

If you know your data includes specific document types, name them:

"Look through our customer support tickets from January 2024 for common complaints about the mobile app."

"Search our meeting transcripts for discussions about the new pricing model."

This helps the retrieval component target the right sources rather than pulling irrelevant information.

Ask for Citations and Sources

One of RAG's biggest advantages is traceability. Your prompts should leverage this:

"What were the main findings from our user research? Please cite which research documents each finding comes from."

This not only gives you the answer but also lets you verify it against the source material—crucial for business decisions.

Structure Multi-Part Queries Carefully

When you need information from multiple sources, break it down:

"First, search our sales data for Q4 2023 revenue by product line. Then search our Q4 strategy documents for our revenue targets. Finally, compare the actual performance against targets and identify gaps."

This staged approach helps the system retrieve comprehensively before synthesizing.

Specify Recency Requirements

RAG systems can access current data, so use that advantage:

"Search our recent product documentation (updated in the last 30 days) for the latest API endpoint specifications."

"What are our hiring priorities? Only reference strategic planning documents from 2024."

Common RAG Prompting Mistakes

Assuming the AI Already Knows Your Data

Even with RAG, you can't just ask questions as if the AI has everything memorized. You still need to prompt it to search and retrieve.

Wrong: "What did Sarah say in the meeting?" Right: "Search our meeting transcripts for Sarah's comments about the budget proposal in last week's leadership meeting."

Being Too Vague About Sources

Generic prompts lead to generic retrieval:

Vague: "What's our policy?" Specific: "Search our employee handbook for the expense reimbursement policy, specifically regarding international travel."

Not Handling "No Results" Gracefully

Build fallbacks into your prompts:

"Search our technical documentation for information about database migration procedures. If nothing specific is found, indicate which related topics are available in the documentation."

Advanced RAG Prompting Techniques

Comparative Analysis

"Search our quarterly financial reports from 2023. Compare Q1 and Q4 revenue growth and identify the factors that contributed to any differences based on the CEO's quarterly letters."

Synthesis Across Documents

"Review all project post-mortem documents from 2023. Identify recurring challenges that appear in at least three different projects and suggest systemic improvements."

Filtered Retrieval

"Search our customer feedback, but only include responses from enterprise clients (those with over 500 seats). What are their top three feature requests?"

The Practical Advantage

RAG-aware prompting turns AI from a knowledgeable generalist into a specialist in your domain. Whether you're using enterprise tools that implement RAG or building custom solutions, understanding how to prompt these systems means you can:

• Get accurate answers grounded in your actual data

• Verify AI responses against source documents

• Work with current information rather than outdated training data

• Maintain security by keeping proprietary data in your control

As more companies implement RAG systems in 2024, this prompting skill isn't just useful—it's becoming essential for anyone who wants to leverage AI effectively in professional contexts.

Your Next Step

If you're using or building RAG systems, start treating prompts as search queries plus generation instructions. Be explicit, be specific, and always ask for sources. This simple mindset shift will dramatically improve your results.