Playbook / AI system design
AI system design
AI/ML platform design — hellointerview depth
| # | Title | |
|---|---|---|
| 01 | Design an LLM inference serving platform at scale | Study → |
| 02 | Design a retrieval-augmented generation (RAG) platform at scale | Study → |
| 03 | Design an agent orchestration platform with real tool use | Study → |
| 04 | Design a feature store / fine-tuning data pipeline | Study → |
| 05 | Design a content moderation and safety system for generated content | Study → |
| 06 | Design a multimodal search / recommendation system | Study → |
| 07 | Design an LLM evaluation and observability platform | Study → |
| 08 | Design a fine-tuning / RLHF training pipeline at scale | Study → |
| 09 | Design a multi-tenant AI platform | Study → |
| 10 | Design a sandboxing architecture for AI agent code execution | Study → |
| 11 | Design an on-device / edge AI inference architecture | Study → |
| 12 | Design a training-data provenance and IP-risk architecture | Study → |
| 13 | Design durable execution for long-running AI agents | Study → |
| 14 | Design a ChatGPT-style conversational service | Study → |
| 15 | Design an AI coding assistant (Copilot-style) | Study → |
| 16 | Design an LLM-powered customer support assistant | Study → |
| 17 | Design LLM application security against prompt injection | Study → |
| 18 | Design an AI data flywheel and human-feedback platform | Study → |
| 19 | Design a model release, canary, and rollback platform | Study → |
| 20 | Design persistent AI memory and personalization | Study → |
| 21 | Design a real-time voice AI assistant | Study → |
21 of 21 · total playbook 81