What is RAG (Retrieval-Augmented Generation)?

RAG is a technique that enhances AI by connecting it to your business documents. Instead of relying only on training data, the AI retrieves relevant information from your documents before generating responses, resulting in accurate, grounded answers.

How does RAG work for business?

RAG works in three steps: (1) Your documents are processed and stored in a vector database, (2) When a user asks a question, the system finds relevant document sections, (3) The AI generates a response using those specific documents as context.

What is the difference between RAG and fine-tuning?

Fine-tuning trains the AI on your data, which is expensive and requires retraining when documents change. RAG keeps your documents separate and retrieves them at query time, so updates are instant and no retraining is needed.

Is RAG secure for sensitive business documents?

Yes, especially with private RAG deployments. Your documents can stay entirely within your infrastructure using self-hosted vector databases and local LLMs. No data is sent to external APIs.

Enterprise AI guide

RAG for business.

Retrieval-Augmented Generation connects AI to your business documents. Ask questions in plain English, get accurate answers grounded in your actual data.

How RAG works Book Your Call

The basics

What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that makes AI smarter by connecting it to your actual business documents. Instead of relying on what it learned during training, the AI retrieves relevant information from your files before generating a response.

Think of it like giving an AI assistant access to your company's filing cabinet. When someone asks a question, the AI searches your documents first, then uses that specific context to give an accurate answer.

This solves a major problem with standard AI: hallucination. Without RAG, AI invents plausible-sounding answers. With RAG, answers are grounded in your actual documents and can include citations.

Also known as

Enterprise knowledge base AI
Document Q&A systems
Intelligent document search
Private AI search

The process

How RAG works.

Index documents

Your documents (PDFs, docs, emails, wiki pages) are processed and stored in a vector database. This happens once upfront.

Retrieve context

When a user asks a question, the system searches for the most relevant document sections using semantic similarity. Takes milliseconds.

Generate answer

The AI receives the question plus the relevant document sections, then generates an accurate response grounded in your actual data.

Why RAG

Why RAG for business.

Access your own data

Query your internal documents, policies, contracts, and knowledge base using plain English.

Accurate responses

Answers are grounded in your actual documents, eliminating hallucinations and incorrect information.

Always up-to-date

Unlike fine-tuning, RAG uses your current documents. No retraining required when content changes.

Data privacy

Your documents stay in your infrastructure. No training data sent to third parties.

Industries

RAG use cases by industry.

Legal

Contract analysis
Case research
Policy lookup
Compliance Q&A

Healthcare

Medical records search
Protocol lookup
Patient history Q&A
Compliance docs

Finance

Investment research
Regulatory docs
Client portfolio Q&A
Risk analysis

Enterprise

Internal wiki search
HR policy lookup
Technical documentation
Onboarding Q&A

Comparison

RAG vs fine-tuning.

Why RAG is usually the better choice for business documents.

Criteria

Fine-tuning

RAG

Cost

Expensive ($10K-100K+)

More affordable

Document updates

Requires full retraining

Instant, no retraining

Source citations

None

Can cite specific sources

Accuracy

Can still hallucinate

Grounded in your documents

Setup time

Weeks to months

Days to weeks

Build your RAG system.

We build enterprise RAG systems for document Q&A, knowledge bases, and intelligent search. Cloud or fully private deployment.

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