Build and deploy a RAG app with Pinecone Serverless

Retrieval-Augmented Generation (RAG) is a central concept for building LLM applications that reduces hallucinations by grounding them in external data. Moving RAG applications from demos to production, however, presents challenges in vectorstore management, deployment, and observability. A proposed solution combines Pinecone Serverless for scalable, usage-based vector storage with LangServe for rapid deployment. This stack is completed by LangSmith for seamless monitoring, creating a robust pathway for building and deploying production-ready RAG applications.