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What is RAG (Retrieval-Augmented Generation)?

RAG connects a language model to your own documents, database, or knowledge base at the moment you ask a question. Instead of relying only on what the model learned during training, it retrieves the most relevant real content first, then generates an answer grounded in that content.

How It Works

Retrieve, then generate

Index your content

Documents are split into chunks and converted into vector embeddings, stored in a vector database.

User asks a question

The question is also converted into an embedding to compare against your indexed content.

Retrieve relevant chunks

The system finds the most relevant pieces of your content based on similarity to the question.

Generate grounded answer

The LLM receives the retrieved content as context and writes an answer based on it, often with source citations.

Why It Matters

RAG vs. relying on the model's training alone

Answers stay current — update your source documents and the system reflects it immediately, no retraining needed.

Answers can cite their source, which matters for support, legal and compliance use cases where trust is non-negotiable.

Far cheaper to build and maintain than fine-tuning a model on proprietary data.

Reduces hallucination significantly, since the model is answering from real retrieved text rather than guessing from memory.

Where It's Used

Common RAG applications

Customer support chatbots

Answering questions from your actual product documentation and policies, not generic training knowledge.

Internal knowledge search

Giving employees instant answers from company wikis, policies and past reports instead of manual search.

Legal & compliance document Q&A

Answering questions about contracts and case files with citations back to the exact source document.

FAQs

Frequently asked questions

Is RAG the same as fine-tuning?

No. Fine-tuning retrains the model itself on your data. RAG keeps the model unchanged and instead retrieves relevant content at query time — cheaper, faster to update, and easier to keep accurate.

How much data do I need for RAG to work well?

RAG can work with a small set of well-organized documents. Quality and organization of your source content matters more than sheer volume.

Can RAG completely eliminate hallucinations?

It significantly reduces them by grounding answers in real content, but doesn't eliminate them entirely — good RAG systems are designed to say "I don't know" when nothing relevant is retrieved, rather than guessing.

Can you build a RAG system for my business?

Yes — this is one of our core services. Get in touch to discuss your documents and use case.

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