karthikreddy-7/ai-engineering-playbook

应用人工智能工程的零到百学习路径。

RAG ·嵌入 ·向量搜索 ·代理 ·MCP ·以及围绕它们的生产工程

📖 karthikreddy-7.github.io/ai-engineering-playbook

搜索 ·暗黑模式 ·绘制图解 ·自评分测验 ·抽认卡练习 ·进度跟踪

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这是什么

人工智能工程的消费者侧：你对未训练过的模型构建系统 正确、快速、廉价且足够安全，可以直接展示给真实用户。

关于这个主题的大多数材料都是一堆定义。定义告诉你什么是某物 打电话。他们不会告诉你没有它会坏什么，成本多少，或者你会拿什么 ——而这些才是你自己去建设它时最重要的事情。

所以每一页回答三个问题，顺序如下：

（1） 为什么会有这个？

正是这些让这个概念变得必要的具体痛点，以及为什么显而易见的替代方案会失败。

（2） 它到底是怎么工作的？

往下一层，配有一张示意图：机制、参数和重要的数学。

（3） 制作过程中出了什么问题？

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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:

[!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 them from tutorials. It assumes you can code and have called an LLM API.

It’s organized around the questions that decide whether a system works:

• How does RAG actually work — and what does productionizing it involve?
• How does HNSW work, and why cosine similarity?
• How do you evaluate retrieval quality when there’s no single right answer?
• What does document Q&A over 10M documents for 50k users look like?