AI Glossary
RAG (Retrieval-Augmented Generation)
A technique that combines AI text generation with real-time information retrieval from your documents or databases. RAG reduces hallucinations and keeps AI responses grounded in your actual data.
Understanding RAG (Retrieval-Augmented Generation)
RAG is the most practical approach for making AI work with your company's specific data. Instead of training the model on your data (expensive and quickly outdated), RAG retrieves relevant documents at query time and feeds them to the model as context.
The architecture is straightforward: user asks a question, the system searches your document store for relevant passages, those passages are included in the prompt alongside the question, and the AI generates a response grounded in your actual data.
RAG delivers three key benefits: reduced hallucinations (responses cite your documents), always-current information (no retraining needed when data changes), and data security (your documents stay in your infrastructure, only relevant snippets are sent to the model).
RAG (Retrieval-Augmented Generation) in Canada
RAG architecture is particularly valuable for Canadian businesses because it keeps sensitive data within Canadian infrastructure while still leveraging US-hosted AI models for generation.
RAG (Retrieval-Augmented Generation) vs Fine-Tuning: What's the Difference?
| Dimension | RAG (Retrieval-Augmented Generation) | Fine-Tuning |
|---|---|---|
| Definition | Retrieves relevant documents at query time and feeds them to the model as context | Retrains the model on your specific data to change its default behavior |
| Data Freshness | Always current — pulls the latest documents on every query | Frozen at training time — requires retraining when data changes |
| Use Case | Factual Q&A over company documents, policies, knowledge bases | Consistent tone, domain-specific jargon, or specialized output formats |
| Setup Effort | Moderate — needs a vector database and document ingestion pipeline | High — requires curated training examples, evaluation, and iteration |
| Cost | $5K-$30K for pipeline setup; ongoing embedding and retrieval costs | $10K-$50K+ per training run; must retrain as requirements evolve |
Frequently Asked Questions
RAG is best when your data changes frequently and you need factual accuracy grounded in documents. Fine-tuning is better when you need consistent style, tone, or behavior across outputs. Many systems use both.
PDFs, Word documents, web pages, Slack messages, email archives, database records, wiki pages — any text-based content. With multimodal models, RAG can also work with images and diagrams.
See RAG (Retrieval-Augmented Generation) in Action
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