Fine-tune a domain-specific LLM and train custom embedding models on PAI, build a vector search pipeline with OpenSearch/Elasticsearch and OSS, deploy the inference backend via Bailian, and deliver a polished chatbot UI on Vercel with infrastructure managed by Terraform.
Fine-tune a domain-specific LLM and train custom embedding models on PAI, build a vector search pipeline with OpenSearch/Elasticsearch and OSS, deploy the inference backend via Bailian, and deliver a polished chatbot UI on Vercel with infrastructure managed by Terraform.
See _combos/custom-rag-train-embeddings-to-production-app-9bbc6d.
See _combos/custom-rag-pipeline-train-embeddings-to-deploy-a-956ae5.
See _combos/custom-rag-pipeline-with-deployed-frontend-ba57d2.
See _combos/full-custom-rag-custom-llm-custom-embeddings-75fbf5.
Q: How do I build a fully custom RAG system with a deployed web frontend? A: You can implement this architecture by fine-tuning a domain-specific LLM and training custom embedding models on PAI, then delivering a polished chatbot UI on Vercel with infrastructure managed by Terraform. The setup connects these components using a vector search pipeline built with OpenSearch or Elasticsearch and OSS, while the inference backend is deployed via Bailian.