Tutorial

Claude knows a lot, but it does not know your company documents, your codebase, or your private data. RAG (Retrieval-Augmented Generation) fixes this. You give Claude relevant context from your own documents, and it answers questions based on that context. This is Article 17 in the Claude AI — From Zero to Power User series. You should know the Messages API before this article. By the end, you will build a working RAG pipeline that answers questions from your own documents. ...

Claude can see your screen and control your computer. It takes screenshots, analyzes what is on screen, then clicks buttons, types text, and navigates applications — just like a human would. This is Article 16 in the Claude AI — From Zero to Power User series. You should know Tool Use before this article. By the end, you will build a working desktop automation that fills out a web form automatically. ...

One agent is powerful. Multiple agents working together are transformative. A code review agent checks for bugs while a test agent writes tests and a documentation agent updates the docs — all in parallel. Multi-agent systems let you break complex tasks into specialized roles. This is Article 15 in the Claude AI — From Zero to Power User series. You should have completed Article 14: Building AI Agents before this article. ...

An AI agent is not a chatbot. A chatbot answers questions. An agent takes actions. It reads files, writes code, runs commands, queries databases, and makes decisions — autonomously. The Claude Agent SDK gives you the same tools that power Claude Code, but programmable. This is Article 14 in the Claude AI — From Zero to Power User series. You should have completed Article 8: Tool Use and Article 13: MCP before this article. ...

Tool use lets Claude call functions you define in your API request. MCP goes further — it lets Claude connect to external servers that provide tools, data, and prompts. MCP is the bridge between Claude and the rest of your infrastructure. This is Article 13 in the Claude AI — From Zero to Power User series. You should have completed Article 8: Tool Use before this article. By the end of this article, you will understand MCP architecture, use pre-built MCP servers, and build your own MCP server in both Python and TypeScript. ...

Some problems need thinking. A complex debugging task, a math proof, or a multi-step architecture decision — these benefit from Claude reasoning through the problem before answering. Extended thinking makes this explicit and controllable. This is Article 12 in the Claude AI — From Zero to Power User series. You should have completed Article 7: Messages API before this article. By the end of this article, you will know how to enable extended thinking, set thinking budgets, and decide when to use it. ...

Every time you call the Claude API with the same system prompt, you pay full price for those input tokens. Prompt caching fixes this. Cache your repeated context once, then pay only 10% on every subsequent call. This is Article 11 in the Claude AI — From Zero to Power User series. You should have completed Article 7: Messages API before this article. By the end of this article, you will know how prompt caching works, when it pays off, and how to implement it in your applications. ...

You ask Claude to return JSON, and it usually works. But sometimes it adds extra text, wraps it in markdown code blocks, or returns invalid JSON. Structured output fixes this — it guarantees valid JSON that matches your schema. This is Article 10 in the Claude AI — From Zero to Power User series. You should have completed Article 7: Messages API before this article. By the end of this article, you will know two ways to get reliable structured data from Claude, and when to use each one. ...

Claude can see. Send it a screenshot, a photo of a document, a chart, or a technical diagram — and it will analyze what it sees. Vision turns Claude into a powerful tool for data extraction, UI review, and document processing. This is Article 9 in the Claude AI — From Zero to Power User series. You should have completed Article 7: Messages API before this article. By the end of this article, you will know how to send images to Claude, extract data from documents, analyze screenshots, and optimize image costs. ...

These ten concepts appear in almost every Python project. If you know all of them, you can read and write real Python code. If you are missing one, that is the one that trips you up on every project. 1. Variables and Data Types Python infers types automatically. No declaration needed. name: str = "Alex" age: int = 25 score: float = 9.5 active: bool = True nothing = None print(type(name)) # <class 'str'> The four built-in collection types: numbers = [1, 2, 3] # list — ordered, mutable point = (10, 20) # tuple — ordered, immutable tags = {"python", "dev"} # set — unique items user = {"name": "Alex", "age": 25} # dict — key/value pairs Use type() to check the type of any variable at runtime. Use type hints for documentation and IDE support. ...