RAG for Business: Building AI That Knows Your Company

There's a fundamental problem with off-the-shelf AI assistants: they don't know anything about your business. Ask ChatGPT about your company policies, product specifications, or internal procedures, and you'll get generic responses or outright fabrications.

Retrieval Augmented Generation (RAG) solves this. It's the technique that turns a general-purpose AI into an expert on your specific domain—without expensive model training or fine-tuning.

The Knowledge Gap Problem

Large language models are trained on vast amounts of public internet data. They know about Shakespeare, programming languages, and world history. But they don't know:

• Your company's HR policies
• Your product technical specifications
• Your customer support procedures
• Your internal documentation
• Anything that happened after their training cutoff

This creates a gap between what AI could do and what it can actually help with in your business.

The Traditional Solutions (And Their Problems)

Fine-tuning means retraining the model on your data. It's expensive, requires ML expertise, and needs repeating whenever your knowledge base changes. For most businesses, it's impractical.

Prompt stuffing means pasting context into every prompt. It works for small amounts of information but hits context limits quickly. You can't paste your entire documentation library into a prompt.

Custom training means building a model from scratch. Unless you're a large tech company, this isn't an option.

RAG offers a better way.

How RAG Works

RAG augments AI responses with relevant information retrieved from your knowledge base. The process:

1. Index your knowledge: Convert your documents into searchable embeddings (vector representations)
2. User asks a question: "What's our refund policy for enterprise customers?"
3. Retrieve relevant context: Search your knowledge base for documents related to the query
4. Augment the prompt: Include the retrieved documents as context for the AI
5. Generate response: AI answers based on your actual documentation

The AI never hallucinates policies because it's working from your real documents.

┌─────────────────────────────────────────────────────────────┐
│                        User Query                           │
│        "What's our refund policy for enterprise?"           │
└─────────────────────────┬───────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                    Vector Search                            │
│     Search embeddings for semantically similar docs         │
└─────────────────────────┬───────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                  Retrieved Context                          │
│   • enterprise-refund-policy.pdf (similarity: 0.92)         │
│   • customer-terms-enterprise.pdf (similarity: 0.87)        │
│   • support-procedures.pdf (similarity: 0.76)               │
└─────────────────────────┬───────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                   Augmented Prompt                          │
│   "Based on these documents: [context]                      │
│    Answer: What's our refund policy for enterprise?"        │
└─────────────────────────┬───────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                    AI Response                              │
│   "Enterprise customers are eligible for full refunds       │
│    within 30 days of purchase, or pro-rated refunds         │
│    within 90 days, as per section 4.2 of the terms..."      │
└─────────────────────────────────────────────────────────────┘

Business Applications

1. Internal Knowledge Assistants

Every company has tribal knowledge trapped in documents, wikis, Slack threads, and people's heads. RAG-powered assistants make this searchable and conversational:

• HR queries: "How many days of paternity leave do I get?"
• IT support: "How do I connect to the VPN from a personal device?"
• Onboarding: "What's the process for requesting equipment?"

Staff get instant, accurate answers instead of hunting through folders or waiting for email replies.

2. Customer Support

Train your support AI on product documentation, FAQs, and historical tickets:

• Answers customer questions accurately
• References specific documentation
• Escalates to humans only when needed
• Maintains consistent messaging

One client reduced support ticket volume by 40% after implementing RAG-based first-line support.

3. Sales Enablement

Give your sales team an AI that knows your products inside out:

• Product specifications and comparisons
• Pricing and configuration options
• Competitive positioning
• Case studies and references

Sales reps can instantly answer technical questions during calls instead of "I'll get back to you on that."

4. Compliance and Legal

For regulated industries, RAG ensures AI responses are grounded in actual policy:

• Regulatory requirements and interpretations
• Internal compliance procedures
• Contract terms and conditions
• Audit requirements

The AI cites sources, so answers are verifiable—critical for compliance contexts.

Implementation Guide

Step 1: Audit Your Knowledge

Before building anything, map your knowledge landscape:

• What documents exist? Policies, procedures, product docs, FAQs
• Where do they live? SharePoint, Confluence, Google Drive, local files
• How current are they? Stale documentation creates stale answers
• Who owns them? You'll need update processes

Most companies discover their knowledge is more scattered and outdated than expected. RAG implementation often drives valuable documentation cleanup.

Step 2: Choose Your Stack

A RAG system needs three components:

Vector Database stores your document embeddings:

• Pinecone, Weaviate (managed, easy to start)
• Chroma, Qdrant (self-hosted options)
• PostgreSQL with pgvector (if you want to stay in Postgres)

Embedding Model converts text to vectors:

• OpenAI embeddings (easy, good quality)
• Cohere embeddings (good for enterprise)
• Open-source models (Sentence Transformers, if you need on-prem)

LLM for Generation:

• Claude (excellent for nuanced, accurate responses)
• GPT-4 (versatile, widely supported)
• Open-source (Llama, Mistral for on-prem requirements)

For most businesses, start with managed services. You can self-host later if needed.

Step 3: Prepare Your Documents

Document preparation is where RAG projects succeed or fail:

Chunking: Split documents into retrievable pieces

• Too small: loses context
• Too large: dilutes relevance
• Typical: 500-1000 tokens with overlap

Metadata: Tag chunks with source, date, category

• Enables filtering ("only search HR docs")
• Improves relevance ranking
• Supports citations in responses

Cleaning: Remove noise that confuses retrieval

• Headers, footers, page numbers
• Redundant boilerplate
• Formatting artifacts

Step 4: Build the Pipeline

A minimal RAG pipeline:

1. Ingestion: Load documents → chunk → embed → store
2. Retrieval: Query → embed → vector search → rank results
3. Generation: Construct prompt with context → call LLM → return response

Start simple. Add sophistication (re-ranking, hybrid search, query expansion) once the basics work.

Step 5: Test and Iterate

Before launching, build a test set:

• 50-100 questions users might ask
• Expected answers (or source documents)
• Edge cases and adversarial queries

Measure retrieval quality (does it find the right documents?) and generation quality (are answers accurate and helpful?). Iterate on chunking, prompts, and retrieval parameters.

Common Pitfalls

Garbage In, Garbage Out

RAG is only as good as your knowledge base. If your documents are outdated, contradictory, or incomplete, your AI will reflect that. Budget time for knowledge base cleanup.

Over-Reliance on Semantic Search

Vector search finds semantically similar content, but sometimes you need exact matches. Hybrid search (combining vector and keyword search) often outperforms pure semantic search.

Ignoring Context Windows

Even with RAG, you're limited by the LLM's context window. If you retrieve too many documents, you'll hit limits or degrade response quality. Be selective about what context you include.

No Source Attribution

Users need to verify AI responses. Always include source citations so users can check the original documents. This builds trust and catches errors.

Stale Indexes

Your knowledge base changes. Your RAG index needs to keep up. Build refresh pipelines—daily or real-time, depending on how quickly your content changes.

Advanced Techniques

Once your basic RAG system works, consider:

Query expansion: Rephrase user queries to improve retrieval. "refund policy" might also search for "returns", "money back", "cancellation".

Re-ranking: Use a separate model to re-score retrieved results for relevance. Often significantly improves answer quality.

Agentic RAG: Let the AI decide what to search for, iterate on queries, and synthesize from multiple searches. More powerful but more complex.

Multi-modal RAG: Include images, diagrams, and tables in your knowledge base. Useful for technical documentation with visual content.

ROI Considerations

RAG implementations typically show ROI through:

• Reduced support volume: 20-50% of queries handled automatically
• Faster employee onboarding: Days, not weeks, to productivity
• Improved consistency: Same answer every time, based on actual policy
• Expert availability: Domain knowledge accessible 24/7, not locked in people's heads

The payback period is often 3-6 months for customer-facing applications, longer for internal tools (harder to measure but still valuable).

Getting Started

If you're considering RAG for your organisation:

1. Start with a focused use case: One department, one document set, one user group
2. Prove value quickly: Pick a use case with measurable impact
3. Plan for maintenance: RAG is not set-and-forget—budget for ongoing updates
4. Build trust gradually: Start with low-stakes queries, expand as confidence grows

The technology is mature enough for production use. The main challenges are organisational: cleaning up knowledge bases, defining ownership, and integrating with existing workflows.

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Ready to build AI that actually knows your business? Contact us to discuss how RAG can transform your knowledge management and customer experience.