AI Engineering · INTERVIEW GUIDE
Practice this topicAI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Start here3AI and LLM fundamentals13Python for AI engineers6Embeddings7Vector databases and search5RAG, beginner to advanced11Agents and agent patterns9LangChain7LangGraph, in depth11Modern agent frameworks4Deterministic and agentic workflows3Workflow orchestration4Model Context Protocol (MCP)6AI memory4AI security9Guardrails8AI evaluation6AI observability4Cost and performance4AI gateways2Multimodal AI4Local AI4Fine-tuning3Cloud AI4MERN and AI16MongoDB and AI3AI system design16Insurance AI reference project7Practical project roadmap10Interview preparation21LLMs4Prompt engineering4RAG4Vector databases4LangGraph4AI agents4MCP4Microsoft Agent Framework4Workflows4Memory4Evaluation4Observability4Cost optimization4Python4FastAPI4Senior engineer decision framework16Interview revision cheat sheet9
2 questions
Gateways, routing, fallback, quotas, and observability
An AI gateway sits between your application and model providers. It can normalize access, choose approved models, enforce quotas, collect usage, and handle eligible fallback. It is similar to an API gateway specialized for model traffic.
Intermediate2 min read
LiteLLM, Portkey, and cloud AI gateways
LiteLLM provides model-access abstraction and proxy/gateway capabilities. Portkey provides an AI gateway and operational controls. Cloud gateways can apply access and model traffic policies within an existing cloud API-management environment; exact AI…
Intermediate2 min read
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