GenAI System Design

Advanced · 6 topics

This series pulls the whole track together. A GenAI system design round asks you to combine everything: retrieval, models, agents, evaluation, cost, latency and safety into one design, and to defend the trade-offs with numbers. Each case study follows the same structure you'd use in an interview, so you can practise the approach, not memorise answers.

Free preview of the full GenAI System Design guide

Each page gives the problem, the requirements, a headline estimate, the architecture and the key insight. The complete guide (a PDF in the Store) adds the full estimates, every component, deep dives, trade-offs, failure modes and model interview answers. See the full guide in the Store.

#	Topic	What it covers
1	An approach for interviews	How GenAI rounds differ · a 7-step framework with timings · questions to ask · a token and cost estimator · what interviewers score
2	Case: customer support chatbot	RAG + tools, handoff to humans, permissions, cost per conversation
3	Case: document Q&A at scale	50 million documents, ingestion throughput, index sizing, multi-tenancy, freshness
4	Case: coding assistant	Sub-second completions, repository context, chat and agent modes, privacy
5	Case: multi-agent workflow	An invoice-processing back office: queues, state, human approval, cost per job
6	Trade-offs and capacity estimates	The decisions that recur in every design, with the maths to back them

flowchart LR
    R[Requirements<br/>quality · latency · cost · safety] --> E[Estimates<br/>QPS · tokens · $ · GPUs]
    E --> A[Architecture]
    A --> D[Deep dives<br/>retrieval · models · agents]
    D --> Q[Evaluation & guardrails]
    Q --> S[Scale · reliability · cost]

Builds on

The Architecture pages (Production RAG, AI agent architecture) explain the building blocks these designs use. The RAG, AI Agents and LLMOps notes go deep on each one.

Back to: Notes overview