How to Implement RAG in Your Business: A Practical Guide

Retrieval-Augmented Generation (RAG) is the most practical way to make LLMs useful for your business. Instead of fine-tuning (expensive, slow) or prompt engineering alone (limited context), RAG lets you ground AI responses in your actual data.

After building RAG systems for 50+ companies, here's what actually works.

What is RAG, Really?

RAG is simple: before asking an LLM to generate a response, you first retrieve relevant documents from your knowledge base and include them in the prompt.

User asks question → Search your docs → Include relevant chunks → LLM generates grounded answer

The result: responses based on your data, not just the model's training data.

The Architecture That Works

Forget the complex diagrams. Here's the architecture that ships:

1. Document Ingestion: PDFs, docs, web pages → chunk into ~500 token pieces
2. Embedding: Convert chunks to vectors using ada-002 or similar
3. Vector Store: Store in Pinecone, Weaviate, or pgvector
4. Retrieval: On query, find top-k similar chunks
5. Generation: Pass chunks + query to LLM for response

Common Mistakes to Avoid

Chunking too large or too small. 500-1000 tokens is the sweet spot. Too small and you lose context. Too large and you waste token budget.

Ignoring metadata. Store source, date, and category with each chunk. You'll need it for filtering and citations.

No hybrid search. Vector similarity alone misses exact matches. Combine with BM25 for better results.

Skipping evaluation. Build a test set of 50+ questions with known answers. Measure retrieval and generation quality separately.

When RAG Isn't Enough

RAG works great for knowledge Q&A. It struggles with:

• Multi-step reasoning across documents
• Tasks requiring structured output
• Real-time data that changes frequently

For these, consider agents, function calling, or fine-tuning.

Getting Started

Start small. Pick one use case—internal knowledge base, customer support, document Q&A. Build a prototype in 2 weeks. Measure results. Iterate.

Need help? That's what we do. Book a call and we'll assess your use case.