What is RAG and Why Does It Matter?
RAG stands for Retrieval-Augmented Generation. In simple terms, it means giving AI access to your company's documents so it can answer questions based on your actual information — not just its general training data.
Without RAG, AI can only draw from public knowledge. With RAG, AI becomes an expert on your company: your policies, your products, your processes, and your market data.
How Business Teams Can Use RAG Today
You don't need to build a custom AI system to benefit from RAG concepts. Here are practical approaches available now:
Method 1: Copy-Paste Context
The simplest form of RAG. Paste relevant document content directly into your prompt.
Based on the following excerpt from our Employee Handbook: [paste relevant section]
Answer this question: Can employees work from home on Fridays? Only use information from the provided text. If the answer is not in the text, say "Not covered in the provided document."
Method 2: Custom GPTs (ChatGPT)
Create a custom GPT with your documents uploaded. The AI will reference these documents when answering questions.
Use cases:
- Company policy Q&A bot
- Product knowledge assistant
- Onboarding guide
- Process documentation helper
Method 3: Microsoft Copilot with M365
If your company uses Microsoft 365, Copilot can access your SharePoint, Teams, and email data.
Use cases:
- "Summarise the key decisions from last Tuesday's project meeting"
- "What does our procurement policy say about vendor approval for purchases over $5,000?"
- "Find all emails about the Singapore expansion project from the past month"
Method 4: Enterprise AI Platforms
Tools like Azure OpenAI Service, AWS Bedrock, or dedicated platforms allow companies to build secure RAG systems over their document repositories.
Effective Prompting with Document Context
Rule 1: Be Specific About Sources
Based ONLY on the provided document, answer this question. Do not use any outside knowledge. If the information is not in the document, state "Not found in the provided document."
Rule 2: Identify the Most Relevant Section
Read this document and identify the sections most relevant to [question]. Quote the relevant text, then provide your analysis.
Rule 3: Cross-Reference Multiple Documents
I am providing 2 documents: Document A: Our AI Usage Policy Document B: Singapore PDPA Guidelines
Compare them and identify:
- Areas where our policy meets PDPA requirements
- Gaps where our policy does not address PDPA requirements
- Recommended additions to our policy
Rule 4: Summarise for Different Audiences
Summarise this 20-page policy document in 3 versions:
- Executive summary (1 paragraph, 100 words)
- Manager briefing (5 bullet points, key actions)
- Employee quick-reference (10 FAQ-style Q&As)
Common Business RAG Use Cases
Policy Q&A
Upload your employee handbook, HR policies, IT policies, and compliance documents. Employees can ask questions and get answers grounded in your actual policies.
Product Knowledge
Upload product documentation, feature specs, pricing guides, and competitive comparisons. Sales and customer service teams get instant, accurate product information.
Training and Onboarding
Upload training materials, SOPs, and process guides. New employees can ask questions and get answers based on your actual procedures.
Research and Analysis
Upload market research, industry reports, and competitive intelligence. Strategy teams can query across multiple documents.
Meeting Intelligence
Use AI to search across meeting notes, action items, and decisions. Find "What did we decide about [topic] in the Q2 planning meeting?"
Data Safety Considerations
What Documents Are Safe to Use with AI
- Internal process documentation (SOPs, workflows)
- Published company policies
- Public-facing marketing materials
- Non-confidential training materials
- General industry research
What Requires Enterprise AI Platforms
- Customer data and contracts
- Financial records
- Employee personal information
- Proprietary algorithms or IP
- Legal documents
Never Upload to Consumer AI
- Personally identifiable information
- Trade secrets and source code
- Pre-release financial data
- Legally privileged communications
Getting Started with RAG
- Start simple: Use copy-paste context for immediate needs
- Build a Custom GPT: Upload 5-10 key company documents for a team Q&A assistant
- Evaluate Copilot: If your company uses M365, explore Copilot's document access capabilities
- Plan enterprise RAG: For larger-scale needs, work with IT to evaluate enterprise AI platforms
The progression from copy-paste to enterprise RAG typically takes 3-6 months, with each step delivering immediate value.
Related Reading
- Copilot M365 Use Cases — Use Microsoft Copilot with your internal M365 documents
- ChatGPT Data Leakage Prevention — Keep sensitive internal data safe when using AI
- Prompting Structured Outputs — Get consistent results when working with document data
Frequently Asked Questions
RAG (Retrieval-Augmented Generation) means giving AI access to your company documents so it answers based on your actual information. Instead of generic knowledge, AI becomes an expert on your policies, products, and processes. It ranges from simply pasting text into prompts to enterprise AI systems connected to your document repository.
Three approaches: (1) copy-paste relevant document text into prompts with clear instructions to use only that source, (2) create Custom GPTs in ChatGPT with uploaded company documents, (3) use Microsoft Copilot which automatically accesses your SharePoint and M365 data. No coding required.
It depends on the document sensitivity and the AI platform. Internal SOPs and published policies are generally safe with enterprise AI tools. Customer data, financial records, and employee PII require enterprise-grade platforms with data processing agreements. Never upload sensitive documents to free or consumer AI tools.