Why RAG beats memory-only chatbots
Retrieval-Augmented Generation pulls answers from your approved sources before the model responds. That grounding is why RAG is the default pattern NexaPhaze uses for support and knowledge assistants.
Core components
You need clean source documents, embeddings, a vector index, prompt contracts and evaluation. Skip evals and quality will drift silently after launch.
- Chunking strategy matched to document types
- Access control so retrieval respects permissions
- Fallback and escalation paths
- Observability on empty retrieval and refusals
Shipping RAG inside a product
Many clients embed assistants inside SaaS products we also engineer. Treating RAG as a product feature — with UX, permissions and metrics — beats bolting a widget onto the marketing site alone.