What is RAG and why does it matter?
RAG (Retrieval Augmented Generation) is the latest generation of AI chatbots: before answering, the system retrieves relevant documents and the LLM generates from them.
Result: more accurate answers, cited sources, minimised hallucination. The Nortinia engine uses two-stage retrieval (semantic + keyword) and re-ranking for maximum precision.
What sets Nortinia RAG apart?
Two-stage retrieval
Semantic + keyword search + re-rank. Higher precision on top-k documents.
Citation
Every answer references the source document — full traceability.
Multi-doc reasoning
Connects multiple documents in the answer — doesn't just copy one snippet.
Eval harness
Built-in eval framework: ground truth, retrieval@k, faithfulness, answer relevancy.
RAG rollout in 4 steps
Document import
URLs, PDFs, Markdown, SharePoint and other document repositories — bulk upload.
Chunking + indexing
Adaptive chunking, vector + keyword index, metadata tagging.
Pilot question set
Eval set to measure retrieval@k and faithfulness.
Go live
A/B-tested prompt fine-tuning, model pinning, monitoring.
RAG chatbot — questions
What is the difference between a "plain" chatbot and RAG?
A plain chatbot answers from the model's trained knowledge (or hallucinates). RAG always retrieves from your documents and generates only from them.
Which formats are accepted?
PDF, DOCX, Markdown, HTML, URL, JSON, CSV, SharePoint, Google Drive and other document repositories. Uploadable via API too.
How does it minimise hallucination?
Mandatory citation, re-rank on top-k, faithfulness scoring in eval, escalation on low confidence.
Does the knowledge base update automatically?
Yes — automatic crawl + diff-based re-indexing. Configurable interval (hourly to daily).
We'll show the RAG chatbot on your own knowledge base.
30-minute demo, eval set, accuracy estimate at the end.