If you’ve been in AI discussions lately, you’ve heard the acronym RAG. It stands for Retrieval-Augmented Generation, and it’s become the standard approach for making AI work with enterprise data.

Here’s what you actually need to understand.

The Problem RAG Solves

Large language models like GPT-4 have a fundamental limitation: they only know what they were trained on. They don’t know your company’s policies, your product documentation, your customer history, or last week’s board meeting.

You could try to train a custom model with your data, but that’s expensive, slow, and the data becomes stale quickly.

RAG offers a different approach: instead of putting your knowledge into the model, you retrieve relevant knowledge at query time and provide it to the model as context.

Think of it like this: the model is a smart person, but they haven’t read your company wiki. RAG gives them access to search the wiki before answering.

How RAG Works (Simplified)

1. Your data is indexed: Documents are broken into chunks, converted to numerical representations (embeddings), and stored in a searchable database.

2. User asks a question: “What’s our return policy for international orders?”

3. Relevant chunks are retrieved: The system searches for document chunks most relevant to the question and finds your international returns policy document.

4. Context is provided to the model: The retrieved chunks are given to the model along with the question.

5. Model generates an answer: Using both its general capabilities and the specific retrieved context, the model answers the question.

The key insight: the model doesn’t need to have memorised your data. It just needs relevant context provided at query time.

Why RAG Matters for Enterprise

Several reasons RAG has become the dominant approach:

Freshness: Unlike fine-tuned models, RAG uses current documents. Update your source documents and the answers change immediately.

Control: You control exactly what data the AI can access. Sensitive documents can be excluded. Access can be restricted by user role.

Attribution: Because RAG retrieves specific documents, you can cite sources. “Based on your international returns policy document dated November 2024…”

Cost: RAG is dramatically cheaper than custom model training. Most enterprises can implement RAG systems for a fraction of fine-tuning costs.

Accuracy: For factual questions about specific content, RAG significantly reduces hallucination. The model isn’t guessing; it’s reading your actual documents.

What RAG Doesn’t Solve

RAG isn’t magic. Important limitations:

Garbage in, garbage out: If your source documents are wrong, poorly written, or contradictory, RAG will reflect that.

Retrieval quality matters: If the system retrieves irrelevant chunks, the answer will suffer. Retrieval is often the weak link.

Complex reasoning struggles: RAG works best for factual lookup. Multi-step reasoning that requires synthesising many documents is harder.

Context limits: There’s a limit to how much context can be provided. Very complex questions requiring massive context may not work well.

Latency: Retrieval adds time. Real-time applications need to account for the retrieval step.

Enterprise RAG Considerations

If you’re evaluating RAG for your organisation:

Data Preparation

Your documents need to be:

• Accessible: Can the system actually read your files? PDFs, Word docs, web pages, databases all require different handling.
• Clean: Documents with OCR errors, formatting issues, or outdated content create problems.
• Chunked appropriately: How you split documents affects retrieval quality. This is more art than science.

Plan for significant data preparation effort. It’s typically 30-40% of a RAG implementation.

Security and Access Control

Enterprise RAG needs to respect your existing access controls:

• Can users only query documents they’re authorised to see?
• How are permissions managed as documents change?
• What audit trail exists for queries?

This is often underestimated. Building proper access control adds significant complexity.

Platform Choices

RAG can be built on various platforms:

• Cloud AI services: Azure OpenAI, AWS Bedrock, Google Vertex all offer RAG capabilities
• Vector databases: Pinecone, Weaviate, Chroma for the retrieval layer
• Enterprise platforms: Tools from AI consultants Melbourne at Team400 package RAG with enterprise features
• Open source: LangChain and similar frameworks for custom implementations

The choice depends on your existing infrastructure, compliance requirements, and internal capability.

Evaluation and Testing

How do you know if your RAG system works well?

• Retrieval testing: Are the right documents being retrieved for different queries?
• Answer quality: Are answers accurate, complete, and appropriate?
• Failure cases: What happens when there’s no relevant document? Does the system admit ignorance or hallucinate?

Build evaluation frameworks before deployment. RAG quality varies significantly with implementation quality.

The Implementation Path

A typical RAG implementation:

Phase 1 (4-8 weeks): Pilot

• Select a contained document set (one department, one topic)
• Build basic RAG pipeline
• Test with real users
• Learn what works

Phase 2 (8-12 weeks): Expansion

• Broader document coverage
• Better chunking and retrieval based on pilot learning
• Access control implementation
• Integration with workflows

Phase 3 (Ongoing): Production

• Full deployment
• Monitoring and measurement
• Continuous improvement
• Document maintenance processes

Don’t skip the pilot. What works in demo doesn’t always work in production.

Questions to Ask Vendors

If you’re evaluating RAG solutions:

1. How do you handle document permissions?
2. What document formats are supported?
3. How do you handle document updates?
4. What happens when there’s no relevant document?
5. How do you measure retrieval quality?
6. What’s the latency for typical queries?
7. Can we see retrieval results (sources) alongside answers?

Good RAG vendors welcome these questions. Evasive answers are concerning.

Final Thought

RAG represents a practical, achievable way to make AI work with your enterprise data. It’s not as flashy as some AI capabilities, but it solves real problems.

The key is understanding that RAG is a system, not a product. Data quality, retrieval tuning, and access control all matter. Implementation quality varies dramatically.

Done well, RAG can transform how employees access organisational knowledge. Done poorly, it creates another system people don’t trust.

The difference is in the details.