Design a retrieval-augmented generation system
Answer questions from a changing document collection with traceable sources.
AI engineering interviews ask you to design products on large language models. These rounds cover ChatGPT, retrieval-augmented generation (RAG) over company documents, a customer support AI agent, an AI code assistant, a real-time voice agent, text-to-image generation, document ingestion for RAG, an AI answer engine, an LLM gateway, an LLM inference platform and an LLM evaluation platform.
Practice what interviewers probe: estimating in tokens, context and retrieval design, tool contracts and approvals for agents, evaluation with golden datasets and calibrated judges, guardrails and prompt injection, streaming and GPU capacity, and cost per task.
Answer questions from a changing document collection with traceable sources.
Serve a chat assistant to millions of people with streamed answers, saved history and fair usage limits.
Resolve support chats with tools and refunds within policy, and hand off to a human when needed.
Suggest code as developers type, using context from their repository, in a few hundred milliseconds.
Answer phone calls with an AI that listens, books appointments and replies without awkward pauses.
Turn prompts into images in seconds for millions of users while keeping GPUs busy and output safe.
Parse, chunk and index millions of PDFs, scans and office files so LLM apps can search them.
Answer questions from the live web with cited sources in a few seconds, like Perplexity.
Give every team one safe way to call many model providers, with routing, fallbacks and budgets.
Serve open-weight LLMs on a GPU fleet with high throughput and predictable latency.
Let teams test prompt, model and agent changes against golden datasets before they ship.