AI FundamentalsRAG

Retrieval-Augmented Generation

RAG

Retrieval-Augmented Generation (RAG) is an AI architecture that connects a Large Language Model with an external knowledge base. Before each response, the system retrieves relevant documents and uses them as context — producing fact-based, verifiable answers without hallucinations, ideal for internal company knowledge.

Why does this matter?

RAG enables businesses to let AI systems access their own data without incorporating it into model training. Contracts, manuals, product datasheets — everything stays in your infrastructure yet is leveraged by the AI. This builds trust and ensures GDPR compliance.

How IJONIS uses this

We implement RAG systems with vector databases like pgvector and Pinecone, integrated into your existing IT landscape. Our architecture includes chunking strategies, hybrid search, and reranking — for precise results even with large document collections.

Frequently Asked Questions

How does RAG differ from a traditional search engine?

A search engine returns document lists for keywords. RAG understands the meaning of your question, finds semantically matching text passages, and formulates a coherent answer — with source citations. This eliminates manually reviewing dozens of results.

What data sources can be connected to a RAG system?

Virtually all: PDFs, Word documents, emails, wiki pages, ERP data, CRM entries, and databases. The key is a clean ETL pipeline that converts your data into embeddings and keeps them up to date.

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