Designing private network connectivity for RAG-capable gen AI apps

Google Cloud offers flexible architecture for building secure AI workloads, particularly for retrieval-augmented generation (RAG) applications. RAG enhances Large Language Models by grounding them in specific knowledge bases, improving accuracy and reducing hallucinations. This approach avoids retraining the model while using designated sources of truth. The example design uses private connectivity, preventing internet traversal. The architecture includes a routing project, a Shared VPC host, and service projects for data ingestion, serving, and frontend. Cloud Interconnect or Cloud VPN provides secure connections, while Network Connectivity Center manages connectivity via VPC and hybrid spokes. Private Service Connect allows private access to Cloud Storage, and Google Cloud Armor/Load Balancer protect user interaction. VPC Service Controls mitigate data exfiltration risks, creating a managed security perimeter. The green dashed line shows data ingestion flow, moving data from external networks to the RAG datastore. The orange dashed line shows inference flow, detailing customer requests through the system. Network Connectivity Center manages control plane and route orchestration via blue dotted lines. The architecture document, including IAM permissions and deployment considerations, should be reviewed. Resources like the Cross-Cloud Network are recommended for further exploration.