How do you build a cloud RAG pipeline for AI?

All answers to this question.

Modern engineering roadmaps emphasize context-aware systems. To build a production-grade RAG pipeline, use managed foundation services like Amazon Bedrock or Azure OpenAI Service. First, configure an ingestion process that chunks enterprise documents and extracts vector embeddings using specialized models. Store these vectors in a managed database like Amazon OpenSearch or Azure AI Search. Your application architecture can then intercept user questions, query the vector store for semantic context, and pass the combined prompt securely to the LLM for accurate generation.

   Answered 2025-10-12 by Evelyn Fowler

Should we use built-in, fully managed cloud RAG tools, or assemble custom orchestrators using frameworks like LangChain inside containers?

   Answered 2025-10-15 by Frank Hansen

• Frank, choosing custom orchestration via frameworks like LangChain or LlamaIndex gives you significantly more control over chunking strategies, metadata filtering, and re-ranking steps. Fully managed services are fantastic for rapid prototyping, but custom setups inside containerized microservices allow you to swap components easily as underlying models evolve.

     Commented 2025-10-16 by Philip Ruiz

Securing data transmission between your cloud vector store and the foundational language model using private endpoints is absolutely mandatory.

   Answered 2025-10-19 by Theresa Ramos

• Spot on, Theresa. Data privacy is a massive concern for enterprises, and private endpoints keep sensitive data off the public web entirely.

     Commented 2025-10-20 by Gregory Olson