RAG knowledge assistant
February 2026 · from the workshop
n8n DevRel build · retrieval-augmented AI Generic AI chatbots give generic answers. When your team or customers need responses grounded in your actual data — internal docs, SOPs, product documentation — you need Retrieval-Augmented Generation. I built a RAG assistant that answers from your own data with high accuracy.
It runs on Weaviate vector search with production-grade RAG: tuned chunking, metadata filtering, and carefully chosen embedding models. Documents are ingested, split into semantically meaningful chunks, embedded, and made instantly searchable — and every question retrieves the most relevant context before the model answers.