Glossary / AI and Machine Learning
RAG (Retrieval Augmented Generation)
The technique that makes AI accurate by grounding it in your specific business data before generating a response.
Definition
Retrieval Augmented Generation (RAG) is a technique that improves AI responses by first searching a knowledge base for relevant information, then feeding that context to a large language model to generate accurate, grounded answers. RAG reduces hallucinations and allows AI systems to work with up-to-date, domain-specific data without retraining the model.
How RAG works (simplified)
| Step | What happens | Example |
|---|---|---|
| 1. Query | User asks a question | "What is your refund policy?" |
| 2. Retrieve | System searches your knowledge base for relevant documents | Finds your refund policy page, returns FAQ, and customer service guidelines |
| 3. Augment | Retrieved documents are added to the AI's context | AI now has your specific refund policy in front of it |
| 4. Generate | AI generates a response using your data | "Our refund policy allows returns within 30 days for a full refund..." |
RAG vs fine-tuning vs prompt engineering
| Approach | What it does | Cost | Best for | Accuracy |
|---|---|---|---|---|
| RAG | Retrieves your data at query time | £1,500 to £8,000 | Customer support, internal knowledge, product info | High (grounded in real data) |
| Fine-tuning | Retrains the model on your data | £5,000 to £50,000 | Specialised language, industry jargon, consistent tone | Medium (can still hallucinate) |
| Prompt engineering | Crafts better instructions for the AI | £500 to £2,000 | Quick improvements, simple tasks, prototyping | Variable |
Why RAG matters for your business
Without RAG, an AI chatbot or agent can only answer based on its general training data. It does not know your products, your prices, your policies, or your processes. This leads to:
- Hallucinations: The AI makes up answers that sound plausible but are wrong
- Generic responses: Customers get Wikipedia-level answers instead of your specific information
- Trust erosion: One wrong answer can undermine customer confidence in the entire system
RAG solves all three problems by ensuring the AI always has your actual data in front of it before it responds.
What does RAG implementation cost?
| Component | Cost range | Notes |
|---|---|---|
| Knowledge base setup | £500 to £2,000 | Indexing your documents, FAQs, product data |
| Vector database | £0 to £50/month | Pinecone, Weaviate, or self-hosted options |
| RAG pipeline development | £1,500 to £5,000 | Building the retrieval and generation workflow |
| Testing and refinement | £500 to £1,500 | Tuning retrieval accuracy, testing edge cases |
| Ongoing API costs | £10 to £100/month | Depends on query volume and model choice |
When NOT to use RAG
- When your data changes by the minute: RAG works best with relatively stable knowledge bases. For real-time stock prices or live inventory, direct API calls are better.
- When you need consistent tone over accuracy: If matching a specific brand voice matters more than factual precision, fine-tuning may be the better approach.
- When the task is simple: If a chatbot only needs to answer 20 FAQs, a rule-based system or simple prompt engineering may be sufficient and cheaper.
Related Terms
- AI Agent - Software that acts on your behalf, making decisions and completing multi-step tasks without constant human oversight.
- Prompt Engineering - The practice of writing instructions that get reliable, useful outputs from AI systems.
- Agentic AI - AI systems that act autonomously to achieve goals, making decisions and executing multi-step plans.
- LLM Citation - How AI systems decide which websites to reference in their responses.
Ready to put AI to work in your business?
Book a free 30-minute discovery call. We will assess your data readiness, identify where RAG could improve your AI systems, and give you a clear picture of what it would cost and how long it would take.
Definition
Retrieval Augmented Generation (RAG) is a technique that improves AI responses by first searching a knowledge base for relevant information, then feeding that context to a large language model to generate accurate, grounded answers. RAG reduces hallucinations and allows AI systems to work with up-to-date, domain-specific data without retraining the model.
How RAG works (simplified)
| Step | What happens | Example |
|---|---|---|
| 1. Query | User asks a question | "What is your refund policy?" |
| 2. Retrieve | System searches your knowledge base for relevant documents | Finds your refund policy page, returns FAQ, and customer service guidelines |
| 3. Augment | Retrieved documents are added to the AI's context | AI now has your specific refund policy in front of it |
| 4. Generate | AI generates a response using your data | "Our refund policy allows returns within 30 days for a full refund..." |
RAG vs fine-tuning vs prompt engineering
| Approach | What it does | Cost | Best for | Accuracy |
|---|---|---|---|---|
| RAG | Retrieves your data at query time | £1,500 to £8,000 | Customer support, internal knowledge, product info | High (grounded in real data) |
| Fine-tuning | Retrains the model on your data | £5,000 to £50,000 | Specialised language, industry jargon, consistent tone | Medium (can still hallucinate) |
| Prompt engineering | Crafts better instructions for the AI | £500 to £2,000 | Quick improvements, simple tasks, prototyping | Variable |
Why RAG matters for your business
Without RAG, an AI chatbot or agent can only answer based on its general training data. It does not know your products, your prices, your policies, or your processes. This leads to:
- Hallucinations: The AI makes up answers that sound plausible but are wrong
- Generic responses: Customers get Wikipedia-level answers instead of your specific information
- Trust erosion: One wrong answer can undermine customer confidence in the entire system
RAG solves all three problems by ensuring the AI always has your actual data in front of it before it responds.
What does RAG implementation cost?
| Component | Cost range | Notes |
|---|---|---|
| Knowledge base setup | £500 to £2,000 | Indexing your documents, FAQs, product data |
| Vector database | £0 to £50/month | Pinecone, Weaviate, or self-hosted options |
| RAG pipeline development | £1,500 to £5,000 | Building the retrieval and generation workflow |
| Testing and refinement | £500 to £1,500 | Tuning retrieval accuracy, testing edge cases |
| Ongoing API costs | £10 to £100/month | Depends on query volume and model choice |
When NOT to use RAG
- When your data changes by the minute: RAG works best with relatively stable knowledge bases. For real-time stock prices or live inventory, direct API calls are better.
- When you need consistent tone over accuracy: If matching a specific brand voice matters more than factual precision, fine-tuning may be the better approach.
- When the task is simple: If a chatbot only needs to answer 20 FAQs, a rule-based system or simple prompt engineering may be sufficient and cheaper.
Related Terms
- AI Agent - Software that acts on your behalf, making decisions and completing multi-step tasks without constant human oversight.
- Prompt Engineering - The practice of writing instructions that get reliable, useful outputs from AI systems.
- Agentic AI - AI systems that act autonomously to achieve goals, making decisions and executing multi-step plans.
- LLM Citation - How AI systems decide which websites to reference in their responses.
Ready to put AI to work in your business?
Book a free 30-minute discovery call. We will assess your data readiness, identify where RAG could improve your AI systems, and give you a clear picture of what it would cost and how long it would take.
Frequently Asked Questions
Common questions about RAG and knowledge-grounded AI.
Does RAG completely eliminate AI hallucinations?
It significantly reduces them but does not eliminate them entirely. RAG ensures the AI has accurate source material, but the generation step can still occasionally misinterpret or combine information incorrectly. Good implementations include citation tracking (so users can verify sources) and confidence scoring (so the system knows when to escalate to a human).
What data formats can RAG work with?
RAG can work with almost any text-based data: PDFs, Word documents, web pages, spreadsheets, emails, CRM records, knowledge base articles, and product databases. Images and video require additional processing steps but can also be included. The key requirement is that the information can be converted to searchable text.
Is RAG better than fine-tuning?
For most business applications, yes. RAG is cheaper, faster to implement, easier to update (just add new documents), and produces more factually grounded responses. Fine-tuning is better when you need the AI to adopt a specific writing style or understand specialised terminology that does not exist in general models. Many production systems use both: RAG for accuracy and fine-tuning for tone.