What does RAG mean?
RAG means retrieval-augmented generation: an AI approach that retrieves source information before generating an answer.
MIP Technologies Ltd
RAG Knowledge Base AI Systems
A RAG knowledge base AI system connects a language model to your company documents, product data, policies or internal knowledge base. Instead of relying only on generic model knowledge, it retrieves relevant information from approved sources and generates grounded answers for customers, employees or sales teams.
What is RAG?
RAG means retrieval-augmented generation. It retrieves information from approved business sources before generating an answer.
Why businesses use RAG systems
Businesses use RAG to make AI answers more grounded, controllable and connected to current company knowledge.
Customer support use cases
RAG can help answer policy, product, service and troubleshooting questions using approved customer-facing sources.
Internal knowledge base use cases
Teams can search documents, policies, onboarding material and operational knowledge through a conversational interface.
Product catalog and ecommerce use cases
RAG can retrieve product attributes, category information, size guides, policy pages and catalog data for more accurate answers.
Retrieval, citations and source control
A good RAG system should control which sources are searchable and, where appropriate, show references for generated answers.
Data security and tenant isolation
MIP Technologies designs retrieval boundaries so each tenant, customer or team can be kept separate when needed.
Implementation process
Implementation includes source mapping, ingestion, chunking, retrieval testing, answer design, access rules and deployment.
Frequently asked questions
Why is RAG useful for company AI systems?
It lets AI systems answer from approved business knowledge instead of relying only on generic model training.
Can RAG reduce hallucinations?
RAG can reduce unsupported answers by grounding responses in approved sources, although it must still be tested and monitored.
What sources can be connected?
Web pages, PDFs, product catalogs, policy documents, databases and knowledge bases can be connected depending on access and quality.
Can it work with PDFs, websites, product catalogs and databases?
Yes. These are common source types for business RAG systems.
How do you keep tenant data separated?
Tenant separation can be designed through data partitioning, access rules, scoped indexes and retrieval boundaries.
Can answers include source references?
Yes. Answers can include source references when the retrieval and UI are designed to expose them.