{"repo":"karthikreddy-7/ai-engineering-playbook","free":true,"listed":false,"github":"https://github.com/karthikreddy-7/ai-engineering-playbook","clone":"git clone https://github.com/karthikreddy-7/ai-engineering-playbook.git","description":"A zero-to-100 learning path for applied AI engineering — RAG, embeddings, vector search, agents, MCP, and the production engineering around them. 56 pages, built as a searchable site.","language":"MDX","stars":57,"topics":["ai-agents","ai-engineering","embeddings","learning-resources","llm","llmops","mcp","mkdocs-material","rag","retrieval-augmented-generation"],"license":"MIT","category":"ai-agents","readme_excerpt":"AI Engineering Playbook A zero-to-100 learning path for applied AI engineering. RAG · embeddings · vector search · agents · MCP · and the production engineering around them 📖 karthikreddy-7.github.io/ai-engineering-playbook Search · dark mode · rendered diagrams · self-graded quizzes · flashcard drills · progress tracking --- What this is The consumer side of AI engineering: you take models you did not train and build systems that are correct, fast, cheap, and safe enough to put in front of real users. Most material on this subject is a pile of definitions. Definitions tell you what something is called. They don't tell you what breaks without it, what it costs, or what you'd reach for instead — and those are the things that matter when you're the one building it. So every page answers three questions, in order: ① Why does this exist? The specific pain that makes the concept necessary, and why the obvious alternatives lose. ② How does it actually work? One level down, with a diagram: the mechanism, the parameters, and the maths that matter. ③ What goes wrong in production? Scale, cost, latency, failure modes — the things that only show up under real traffic. [!NOTE] This is not a machine-learning course. No PyTorch, no training loops, no backprop. Model internals appear only at survival depth — enough to reason about a boundary question without bluffing. Who it's for Engineers building at the application layer who want to understand these systems properly rather than assemble","default_branch":null,"files":null,"tree":[],"storefront":"/r/karthikreddy-7","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/karthikreddy-7/ai-engineering-playbook/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}