amitshekhariitbhu/ai-system-design
AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step.
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README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
AI System Design - A complete guide to learn AI System Design step by step - from LLM inference, GPUs, KV Cache, and caching to RAG, Vector Databases, AI Agents, MCP, Multi-Agent Systems, Voice AI, Guardrails, Evaluation, Observability, Cost Optimization, and a step-by-step framework to crack any AI System Design interview. Everything in one place, explained in simple words, with detailed blogs for every deep dive. In this guide, we will learn about AI System Design, the discipline of putting GPUs, inference servers, caches, vector databases, AI agents, gateways, guardrails, and evals together into one system that is fast, cheap, reliable, and safe. We will also see how an LLM actually runs on a GPU, how prefill and decode shape latency, how caching, routing, and batching cut the cost, how RAG and AI Agents are built for production, how we keep the system safe and measurable, and a step-by-step framework…
Read the full README ↗ · Preview checked 2026-09-26T12:33:46.297Z
Inside the original README — Document outline
- AI System Design
- Prepared and maintained by the Founder of Outcome School: Amit Shekhar
- Follow Amit Shekhar
- Follow Outcome School
- I teach at Outcome School
- Table of Contents
- About This AI System Design Guide
- What is AI System Design?
- Who is This AI System Design Guide For?
- What Will We Learn in This AI System Design Guide?
- How to Use This AI System Design Guide
- AI System Design Learning Path
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- About This AI System Design Guide
- Who is This AI System Design Guide For?
- What Will We Learn in This AI System Design Guide?
- How to Use This AI System Design Guide
- Inference Server
- Choosing an Inference Engine
- Prefill and Decode: The Two Phases of LLM Inference
- Document Parsing and Ingestion
- A Concrete Example: Customer Support
- Edge AI and On-Device Inference
- Guardrails and Safety
- Evaluation Pipeline
Documentation belongs to its respective authors. Reported project/model license: Apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step.
Repository facts
- Owner
- amitshekhariitbhu
- Primary language
- Markdown
- Stars
- 282
- Forks
- 37
- Open issues + pull requests
- 0
- License
- Apache-2.0
- Archived
- No
- Default branch
- main
- Created
- 2026-09-25T04:50:06.000Z
- Last push
- 2026-09-25T04:53:31.000Z
Topics and intended use
Owner-supplied topics: ai, ai-agents, ai-engineering, ai-system, ai-system-design, ai-systems, ai-systems-design, llm, system-design, system-design-interview
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Source and freshness
Source: GitHub. Metadata observed 2026-09-26T12:03:50.182Z. Daily imports are snapshots, not real-time monitoring.
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