Glossary · Modern AI
RAG
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) combines LLMs with real-time knowledge retrieval from your own data to deliver accurate, grounded, and hallucination-free AI responses.
In short
Retrieval-Augmented Generation (RAG) provides accurate, cited responses grounded in your current business data. Common applications include intelligent customer support and internal knowledge management. BespokeWorks deploys Retrieval-Augmented Generation solutions for UK businesses, typically live within 7 days.
Definition
What is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) solves the fundamental limitation of large language models, their inability to access current or proprietary information. RAG pipelines retrieve relevant documents from your knowledge bases using vector search, then feed that context to the LLM for accurate, organisation-specific responses grounded in your actual data.
According to research from Meta AI, RAG reduces hallucination rates by up to 70% compared to standalone LLMs. Enterprise RAG architectures combine semantic search, vector databases, chunking strategies, and re-ranking to deliver production-grade AI assistants that cite sources and maintain factual accuracy.
BespokeWorks builds production RAG systems that connect your documents, knowledge bases, and business data to powerful LLMs. Our RAG implementations include intelligent chunking, hybrid search, citation tracking, and continuous retrieval quality monitoring, delivering AI that your team can trust.
Where it earns its keep
Real-world applications.
-
Intelligent Customer Support
RAG-powered chatbots that reference actual product documentation, FAQs, and policy documents for accurate, cited answers, reducing support escalations by 60%.
-
Internal Knowledge Management
AI assistants helping employees find information across policies, procedures, and documentation instantly, eliminating hours spent searching through SharePoint, Confluence, or email.
Why it matters
Key benefits.
- Provides accurate, cited responses grounded in your current business data
- Reduces AI hallucinations by up to 70% with document-grounded retrieval
- No expensive model retraining required, works with any LLM via API
See how Retrieval-Augmented Generation fits your business.
Run the free analyser, five minutes, no signup, a personalised three-phase roadmap that includes whether Retrieval-Augmented Generation is a fit.
