ai-engineer-expert

To deploy a production AI system with RAG, use FastAPI patterns with rate limiting and circuit breakers alongside a unified LLM client. This approach handles document ingestion, ChromaDB vector stores, and retrieval-augmented generation for scalable chatbot APIs.

Yes, you can integrate OpenAI and Anthropic models within the same application using a unified LLM client. This client supports chat completions, streaming responses, and function calling across both providers for flexible AI engineering.

The best way to implement Retrieval-Augmented Generation involves ingesting documents into a ChromaDB vector store. This RAG implementation allows a chatbot to retrieve relevant context from your company documentation before generating accurate responses.

You need FastAPI and Pydantic to build production-ready AI endpoints because they provide robust deployment patterns. These frameworks support essential reliability features like rate limiting and circuit breakers for managing LLM API traffic.

Prompt engineering with chain-of-thought and few-shot learning templates works by providing structured system messages. These templates guide the LLM's reasoning process, improving the accuracy and consistency of complex AI responses in production.