Production Personalized RAG with Full Infrastructure

Scenario

A team trains custom embedding models and fine-tunes LLMs on PAI, deploys a hybrid retrieval pipeline with AIRec-powered personalized recommendations via OpenSearch, then provisions and delivers the complete application using Terraform on ECS/RDS, Cloudflare CDN, Vercel frontend, and Supabase backend — covering the full lifecycle from ML training through personalized production delivery.

How the products combine

1. alinux · custom-rag-with-optimized-search-relevance-707e4a — Custom RAG with Optimized Search Relevance

See _combos/custom-rag-with-optimized-search-relevance-707e4a.

2. alinux · full-stack-custom-rag-train-to-production-e68446 — Full-Stack Custom RAG: Train to Production

See _combos/full-stack-custom-rag-train-to-production-e68446.

3. es · ml-powered-semantic-search-pipeline-b3728a — ML-Powered Semantic Search Pipeline

See _combos/ml-powered-semantic-search-pipeline-b3728a.

4. airec · custom-trained-rag-with-personalized-recommendat-224893 — Custom-Trained RAG with Personalized Recommendation Layer

See _combos/custom-trained-rag-with-personalized-recommendat-224893.

Typical questions

• build production personalized RAG with full infrastructure
• train custom models and deploy with Terraform and AIRec
• full stack RAG with recommendation engine and CDN
• PAI training to AIRec personalization with production deployment
• end-to-end RAG app with Vercel frontend and Cloudflare
• 从PAI训练到AIRec个性化推荐加生产级基础设施
• Terraform部署个性化RAG加推荐系统全链路
• custom RAG with recommendation and full infrastructure deployment

Q: How do you build and deploy a production-ready personalized RAG application with full infrastructure? A: You can build and deploy this architecture by training custom models on PAI, integrating AIRec-powered recommendations via OpenSearch, and provisioning the complete stack with Terraform, ECS, RDS, Supabase, Vercel, and Cloudflare. This setup covers the entire lifecycle from machine learning training through personalized production delivery. The implementation is structured around four core skill combos that address optimized search relevance, full-stack training pipelines, semantic search, and personalized recommendation layers.