RAG Prompting: Feed AI Your Own Documents for Accurate Answers
Last month, I needed to answer 50+ questions about our company's 200-page procurement policy for an internal training program. Using ChatGPT normally would have been useless—it doesn't know our specific policies, and making up answers (hallucinating) could cause serious compliance issues.
Instead, I used a technique called Retrieval-Augmented Generation (RAG) prompting. Within 30 minutes, I had accurate, policy-specific answers to all 50 questions, each citing the exact policy section it came from.
The best part? No technical setup, no API integrations, no programming. Just smart prompting that transforms any AI like ChatGPT or Claude into an expert on YOUR specific documents.
What is RAG Prompting?
RAG (Retrieval-Augmented Generation) is a technique where you:
- Retrieve relevant information from your documents
- Feed that information directly into your AI prompt
- Generate answers based solely on the provided information
Think of it as giving the AI an open-book test instead of asking it to recall from memory (which it doesn't actually have).
Why RAG Prompting Matters
The Hallucination Problem:
- ChatGPT and Claude don't have access to your company documents, internal policies, or proprietary data
- When asked about things they don't know, they often "hallucinate"—generate plausible-sounding but incorrect information
- This makes them unreliable for business-critical questions
The RAG Solution:
- Provide the exact information the AI should use
- Explicitly instruct it to answer ONLY from provided content
- Get accurate, verifiable answers you can trust
The Basic RAG Prompting Template
Here's the fundamental structure:
You are an expert analyst. Answer the following question using ONLY the information provided in the context below. If the answer cannot be found in the context, say "I cannot find this information in the provided context." CONTEXT: [Your document text goes here] QUESTION: [Your specific question] ANSWER:
Example: Analyzing a Company Policy
Bad Approach (No RAG):
What is our company's policy on remote work equipment reimbursement?
Problem: ChatGPT doesn't know your company's policy, so it will make up a generic answer or pull from other companies' policies it's seen.
Good Approach (With RAG):
You are analyzing a company policy document. Answer the question using ONLY the information in the context below. If the answer isn't in the context, say so explicitly. CONTEXT: "Section 4.2: Remote Work Equipment Reimbursement Employees approved for full-time remote work are eligible for: - One-time home office setup allowance: $750 - Monthly internet reimbursement: Up to $50 with receipt - Annual equipment refresh: $400 for peripherals Reimbursement requests must be submitted within 60 days of purchase using Form HR-301. Approval required from direct manager for amounts over $200." QUESTION: How much can I get reimbursed for internet when working remotely, and what do I need to provide? ANSWER:
Response:
According to the policy, you can get reimbursed up to $50 per month for internet costs when working remotely. You need to provide a receipt with your reimbursement request. The request must be submitted within 60 days of the expense using Form HR-301.
Notice how the AI:
- Answered ONLY from the provided context
- Cited specific amounts and requirements
- Didn't add outside information or assumptions
Advanced RAG Prompting Techniques
Technique 1: Multi-Document RAG
When you need to synthesize information from multiple sources:
You are a research analyst. Answer the question by analyzing ALL provided documents. Cite which document each piece of information comes from using [Doc 1], [Doc 2], etc. DOCUMENT 1 - Q1 2025 Sales Report: [Content from sales report] DOCUMENT 2 - Marketing Campaign Analysis: [Content from marketing report] DOCUMENT 3 - Product Launch Timeline: [Content from product timeline] QUESTION: What factors contributed to the 30% increase in Q1 sales? ANSWER (cite sources):
Why this works:
- The AI can connect dots across multiple documents
- Source citations make answers verifiable
- You see which documents influenced the answer
Technique 2: Comparative RAG Analysis
Compare different versions, policies, or approaches:
You are a policy analyst. Compare the two policy versions below and explain key changes. OLD POLICY (2024): [Old policy text] NEW POLICY (2026): [New policy text] ANALYSIS REQUESTED: 1. What are the 3 most significant changes? 2. How do these changes affect remote workers? 3. Are there any new requirements? COMPARISON:
Use cases:
- Contract revisions
- Policy updates
- Product feature comparisons
- Competitive analysis
Technique 3: RAG with Specific Output Format
Control exactly how information is presented:
You are extracting information from meeting notes. Create a structured summary using ONLY information from the notes below. MEETING NOTES: [Your meeting transcript or notes] Create a summary in this exact format: **Decisions Made:** - [List each decision] **Action Items:** - [Person] will [action] by [date] **Open Questions:** - [List unresolved questions] **Next Steps:** - [List next steps] If any section has no information, write "None identified in notes." STRUCTURED SUMMARY:
This ensures:
- Consistent formatting across multiple documents
- No information is added or assumed
- Easy scanning and sharing
Technique 4: RAG with Confidence Levels
Add confidence scoring to understand answer reliability:
You are analyzing a technical document. Answer the question and rate your confidence in the answer. CONTEXT: [Document text] QUESTION: [Your question] Provide your answer in this format: ANSWER: [Your answer based on context] CONFIDENCE: [High/Medium/Low] - High: Answer is explicitly stated in context - Medium: Answer requires reasonable inference from context - Low: Answer requires significant interpretation or partial information REASONING: [Explain why you gave this confidence level]
Example:
ANSWER: The system supports up to 10,000 concurrent users. CONFIDENCE: High REASONING: The document explicitly states in Section 3.2: "Maximum concurrent users: 10,000." This is a direct quote requiring no interpretation.
Real-World RAG Prompting Workflows
Workflow 1: Analyzing Customer Feedback
Scenario: You have 100 customer survey responses and need to identify common themes.
Step 1: Prepare Your Context
Combine multiple responses into structured format:
CUSTOMER FEEDBACK ANALYSIS CONTEXT (15 representative responses): Response 1: "Love the product but customer service took 3 days to respond..." Response 2: "Great features, wish there was a mobile app..." Response 3: "Customer support was unhelpful and slow..." [... continue with 12 more] ANALYSIS REQUEST: 1. Identify the top 5 recurring themes 2. For each theme, provide: - Theme name - Frequency (how many responses mentioned it) - Representative quotes - Severity (High/Medium/Low customer impact) ANALYSIS:
Step 2: Deep-Dive Follow-ups
Using the same feedback context above, answer: 1. What specific improvements to customer service are requested? 2. Which product features are most frequently praised? 3. Are there any critical issues mentioned by multiple customers? For each answer, cite specific response numbers.
Workflow 2: Contract Review and Comparison
Scenario: Comparing vendor contracts to identify differences.
You are a contract analyst. Compare these two vendor agreements and identify differences that could impact costs or service levels. CONTRACT A (Current Vendor): [Full contract text or relevant sections] CONTRACT B (New Vendor Proposal): [Full contract text or relevant sections] COMPARISON FOCUS AREAS: 1. Pricing structure and escalation clauses 2. Service level agreements (SLAs) and penalties 3. Termination clauses and notice periods 4. Liability and indemnification 5. Payment terms For each area, create a comparison table: | Aspect | Contract A | Contract B | Impact | |--------|-----------|-----------|---------| | [Fill based on documents] | RECOMMENDATIONS: Based purely on the contracts provided, which offers better terms and why?
Workflow 3: Compliance Checking
Scenario: Verify a document complies with regulatory requirements.
You are a compliance analyst. Review the document below against the regulatory requirements and identify any gaps. REGULATORY REQUIREMENTS: [List specific requirements from regulation] DOCUMENT TO REVIEW: [Document content] COMPLIANCE ANALYSIS: For each requirement, indicate: ✅ COMPLIANT: [Cite where requirement is met] ⚠️ PARTIAL: [Explain what's missing] ❌ NON-COMPLIANT: [Explain the gap] SUMMARY: - Total requirements: X - Fully compliant: Y - Partial compliance: Z - Non-compliant: A CRITICAL GAPS: [List must-fix items]
Workflow 4: Research Synthesis
Scenario: Synthesize insights from multiple research papers or articles.
You are a research synthesizer. Analyze the three research papers below and create a comprehensive summary answering the key question. PAPER 1: [Title] [Abstract and key findings] PAPER 2: [Title] [Abstract and key findings] PAPER 3: [Title] [Abstract and key findings] KEY QUESTION: What is the consensus on [topic] and where do researchers disagree? SYNTHESIS: **Points of Consensus:** - [Finding] (Papers 1, 2, 3 agree) - [Finding] (Papers 1, 3 agree) **Areas of Disagreement:** - [Issue]: Paper 1 claims X, but Paper 2 suggests Y **Gaps in Research:** - [Topics not covered by any paper] **Practical Implications:** [Based on the papers, what should practitioners do?]
RAG Prompting Best Practices
1. Chunk Large Documents Strategically
Problem: ChatGPT and Claude have context limits (32K-200K tokens depending on model).
Solution: Break large documents into logical chunks:
DOCUMENT SECTION 1 OF 5: Introduction and Overview [First chunk] [Ask your question about this section] --- Then in next prompt: Previously, you analyzed Section 1 (Introduction). Now analyze Section 2. DOCUMENT SECTION 2 OF 5: Technical Specifications [Second chunk] [Ask questions about this section]
Pro tip: Keep a running summary. After each chunk:
Provide a 3-sentence summary of the key points from this section to carry forward.
2. Be Explicit About Boundaries
Always include this instruction:
CRITICAL INSTRUCTIONS: - Answer ONLY using information in the provided context - If information isn't in the context, say "This information is not provided" - Do not use outside knowledge or make assumptions - Cite specific sections when possible
3. Structure Your Context
Make documents scannable:
CONTEXT STRUCTURE: === SECTION 1: PRICING === [Pricing information] === SECTION 2: SERVICE LEVELS === [SLA information] === SECTION 3: TERMS & CONDITIONS === [Terms information] QUESTION: [Your question]
This helps the AI (and you) quickly locate relevant information.
4. Validate Critical Answers
For high-stakes decisions, use validation prompting:
Earlier, you answered that [previous answer]. Please review the context again and verify this answer is accurate. If you find any errors or want to clarify, provide a corrected answer. CONTEXT: [Same context as before] VERIFICATION:
5. Create RAG Prompt Templates
Save templates for recurring use cases:
Meeting Notes Template:
Extract action items from meeting notes using ONLY the information below. MEETING NOTES: [PASTE NOTES HERE] ACTION ITEMS (format: [Owner] will [action] by [date]):
Email Response Template:
Draft a response to the email below using ONLY information from our policy document. POLICY DOCUMENT: [PASTE POLICY] EMAIL TO RESPOND TO: [PASTE EMAIL] DRAFT RESPONSE:
Common RAG Prompting Mistakes
Mistake 1: Vague Context Boundaries
Bad:
Here's some information about our product. What features does it have? [Document text]
Why it fails: The AI might add features from similar products it knows about.
Good:
List the product features mentioned in the document below. Only list features explicitly stated—do not infer or add features. PRODUCT DOCUMENT: [Document text] FEATURES FOUND IN DOCUMENT:
Mistake 2: Mixing RAG with General Knowledge
Bad:
Here's our Q1 report. How does this compare to industry benchmarks? [Report text]
Why it fails: You're asking for information (industry benchmarks) not in your document.
Good: Either provide both documents:
Compare our Q1 performance to industry benchmarks using ONLY the two documents below. OUR Q1 REPORT: [Report] INDUSTRY BENCHMARK REPORT: [Benchmark data] COMPARISON:
Or ask two separate questions:
- RAG question: "What were our Q1 results?" (using your report)
- General question: "What are typical Q1 benchmarks in our industry?" (using AI's knowledge)
Mistake 3: Overloading Context
Bad: Pasting 50 pages of text and asking "What's important here?"
Why it fails: Exceeds context limits or produces superficial analysis.
Good: Either:
- Ask specific questions: "What does this document say about X?"
- Provide focused chunks: "Here's Section 3 about customer support policies..."
- Request structured extraction: "Extract all pricing information in a table"
Mistake 4: Not Requesting Citations
Bad:
What's our refund policy? [Policy document]
Good:
What's our refund policy? Quote the exact text from the policy document in your answer. [Policy document] ANSWER (with direct quotes):
Citations let you verify answers and provide proof.
Advanced: RAG Prompting with Chain of Thought
Combine RAG with reasoning for complex analysis:
You are analyzing a business case. Use the provided information to make a recommendation, showing your reasoning step-by-step. CONTEXT: [Business case documents] QUESTION: Should we proceed with this project? ANALYSIS (think step-by-step): Step 1: What are the projected costs? [Extract from context] Step 2: What are the projected benefits? [Extract from context] Step 3: What are the risks? [Extract from context] Step 4: ROI Calculation [Calculate based on data] Step 5: Recommendation [State recommendation with reasoning] FINAL RECOMMENDATION:
This ensures the AI:
- Breaks down complex questions
- Shows its reasoning
- Uses only provided data at each step
RAG Prompting Checklist
Before running a RAG prompt, verify:
âś… Context is complete: All necessary information is included
âś… Boundaries are clear: Instructions specify to use only provided info
âś… Question is specific: You know exactly what you want to learn
âś… Format is defined: You've specified how you want the answer structured
âś… Citations requested: If needed, you've asked for quotes/sources
âś… Constraints noted: Any limitations or requirements are mentioned
Tools and Integrations
Browser-Based RAG Tools
ChatGPT Plus (File Upload):
- Upload PDFs, Word docs, text files directly
- Ask questions about uploaded files
- Automatically performs RAG
Claude (with Projects feature):
- Add documents to a Project
- Ask questions across multiple documents
- Maintains context across conversation
No-Code RAG Tools
For Teams:
- Notion AI: Ask questions about your Notion workspace
- Glean: Search and ask questions across company documents
- Hebbia: Document analysis for enterprises
For Personal Use:
- ChatPDF: Upload PDFs and ask questions
- Humata: AI document assistant
- LightPDF AI: Free PDF analysis
API/Developer Options
If you're technical, build custom RAG:
- LangChain: Python framework for RAG pipelines
- LlamaIndex: Document indexing and retrieval
- OpenAI Assistants API: Built-in file handling
Frequently Asked Questions
Can I use RAG with confidential documents?
Yes, but be cautious. When using ChatGPT or Claude through their websites:
- OpenAI and Anthropic state they don't use your conversations for training if you have certain settings enabled
- However, your data still passes through their servers
- For highly sensitive data, use:
- Local/self-hosted AI models
- Enterprise agreements with specific data handling terms
- Anonymize data before using public AI services
What's the maximum document size I can use?
Depends on the AI model:
- ChatGPT-4: ~25,000 words (32K tokens)
- Claude Sonnet: ~150,000 words (200K tokens)
- Gemini Pro 1.5: ~1,500,000 words (2M tokens)
For larger documents, chunk them strategically.
Does RAG work better with certain AI models?
Yes:
- Claude (Anthropic): Often better at following "use only provided context" instructions
- GPT-4: Excellent reasoning but more likely to add outside knowledge
- Gemini: Large context window great for huge documents
My recommendation: Claude for strict RAG adherence, GPT-4 for complex reasoning, Gemini for massive documents.
Can I use RAG for real-time data?
RAG works with static documents you provide in the prompt. For real-time data:
- Fetch current data first (via API, database query, etc.)
- Include in your RAG prompt as context
- Or use AI assistants with real-time tool/plugin access
Related articles: Chain of Thought Prompting, COSTAR Framework: Prompt Structure
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