Architecture Asset: Generative AI with Retrieval-Augmented Generation on STACKIT

Overview

This pattern delivers grounded generative AI. Documents are embedded into an OpenSearch vector store; at query time the app retrieves relevant context and calls a served LLM, keeping data and inference on sovereign infrastructure.

Typical use case

• Knowledge assistants: answer questions grounded in internal documents.
• Support automation: draft responses from trusted knowledge sources.
• Sovereign GenAI: run retrieval and inference with control over data and residency.

Architecture diagram

Design best practices

• Ground every answer: retrieve from the vector store before generation to reduce hallucination.
• Keep data sovereign: run embedding, retrieval, and inference on STACKIT.
• Evaluate continuously: track answer quality, safety, and cost before and after release.
• Secure prompts and secrets: isolate keys and never log sensitive context.