What is retrieval-augmented generation (RAG)?

Retrieval-Augmented Generation (RAG) is an AI technique that combines a retrieval system with a generative AI model. Before generating a response, the AI retrieves relevant information from trusted sources—such as websites, documents, databases, or knowledge bases—and then uses that information to produce a more accurate, relevant, and up-to-date answer.

How RAG works

1. A user asks a question.
2. The AI searches a trusted knowledge source for relevant information.
3. The retrieved content is provided to the AI model.
4. The AI generates a response based on both the retrieved information and its language capabilities.

Benefits of RAG

• Improves the accuracy of AI-generated answers.
• Uses up-to-date information without requiring the AI model to be retrained.
• Reduces hallucinations by grounding responses in reliable sources.
• Provides context-specific and evidence-based answers.
• Enables organizations to use their own documents and knowledge bases with AI.

RAG for jewellery brands

For a jewellery brand, RAG can retrieve information from product pages, buying guides, FAQs, certification details, return policies, and educational content. If a customer asks, “What is the difference between a lab-grown and a natural diamond?”, a RAG-powered AI can retrieve the brand’s published guide and generate an accurate, brand-consistent answer.

By publishing clear, authoritative, and well-structured content, jewellery brands increase the chances that AI systems using RAG can retrieve and reference their information, improving visibility and customer trust.