[1/3] System Design. Preparing for a complex GenAI interview (Category SystemDesign)
I studied interesting. book to prepare for an interview on System Design, but in a new reality, when you need to design not only databases, queues, caches and microservices, but also systems around LLM, diffusion models, RAG, multimodal models and AI-powered products. This is the Russian edition of the book.Generative AI System Design Interviewfrom the ByteByteGo ecosystem. Authors Ali Aminian and Hao Sheng. Ali Aminian is already known for his book about ML System Design Interview, and here the focus shifts from classic ML systems like search and recommendations to generative AI: chatbots, text generation, images, video, RAG and personalized AI scripts.
In a regular System Design Interview, the candidate often draws a distributed system: APIs, balancers, databases, queues, caches, background jobs, monitoring. In a GenAI interview, all this remains, but another layer of complexity emerges:
- What data are needed?
- Which model to choose; Do you need RAG or fine tuning? How to measure the quality of generation; How to deal with hallucinations How to consider latency and the cost of inferencing; How to install safety filters; How to collect feedback loop; How to Monitor System Degradation After Startup
This book is not only useful for ML engineers. It fits well with backend engineers, architects, and technical managers, who now have to design AI features not as a demo on an API, but as part of a production system.
Inside the book are three main things:
1Framework from 7 Steps for GenAI System Design The authors suggest not to start immediately with “take LLM and vector database”, but to consistently go from requirements to deploitation and monitoring in production. This is highly disciplined thinking because in GenAI tasks, it’s easy to jump to trendy technology and forget about the real limitations of the product.
2️⃣ 10 practical problems with detailed solutions Cases include Gmail Smart Compose, Google Translate, ChatGPT-like personal assistant, Image Captioning, Retrieval-Augmented Generation, Realistic Face Generation, High-Resolution Image Synthesis, Text-to-Image Generation, Personalized Headshot Generation and Text-to-Video Generation. This set covers different scenarios and is much broader than just bolting a transformer to a chatbot:)
3Lots of diagrams and end-to-end analysis This is especially important for System Design. A good answer to an interview is not only which model to choose, but also what the system looks like around the model: preprocessing, retrieval, prompt builder, inference service, post-processing, safety layer, logging, monitoring, feedback loop. I think the main value of the book is that it shows that the GenAI system is not a model in a vacuum.
In general, the model is the core of course, but around it there are data, access rights, indexes, prompts, ranking, guardrails, UX, cost, GPU infrastructure, A/B tests, quality metrics and operational limitations. And if all this is not designed consciously, then the output is not a production system, but a beautiful prototype with unpredictable behavior.
The book is useful as a way to update the idea of System Design in the AI era, because before we designed mostly deterministic software: the request came, the service processed, the base responded, the result returned. Systems with probabilistic behavior are increasingly being designed: the model may answer well, average, wrong, dangerous, expensive, or too slowly. Therefore, the architecture should include not only scaling and fault tolerance, but also evaluation, safety, feedback and a continuous improvement loop.
In continuation more detailed analysis of the framework in 7 Steps from the authors of the book.
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