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AI 内容差距分析实战（2026）：嵌入聚类做盘点+LLM 命名、需求挖掘三源（GSC 有曝光无承接页/社区高频问题/工单）、意图级 diff 要求引用现有页防漏判、需求×契合×可赢三轴打分留给人裁决。季度流水线化运行。

LLM 文本去重实战（2026）：三级漏斗——哈希（免费）→嵌入近邻（便宜）→LLM 仲裁灰区（精确），阈值用 200 对标注校准出自动合并/自动判异两条线。含聚类保留策略、增量模式、合并溯源。

LLM 数据增强实战（2026）：安全线=只补「记录内可推导」的字段，外部事实必须走检索。完整异步管线（闭集词表+置信度+null 优先）与三条生产纪律：溯源列、永续抽样 QA、幂等重跑。mini 档模型即胜任。

用 AI 人格做 A/B 测试预筛（2026）：用 LLM 模拟不同用户类型在真流量前预筛文案/设计变体、生成假设。含真实代码、3-6 人格工作流——但模拟≠真实行为，幸存变体仍需真 A/B 验证。

LLM 意图分类实战（2026）：聊天机器人/Agent 的入口。用小模型+固定标签集(Literal/enum)+结构化输出强制返回合法意图，返回置信度做兜底；高并发可用嵌入或微调小模型降本。

AI-Powered Search Engine What You'll Build Building semantic search with vector database. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermedia

AI Summarization Pipeline What You'll Build Summarizing long documents efficiently with map-reduce. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**:

AI Image Analysis Pipeline What You'll Build Analyzing images with GPT-4 Vision API. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermediate

AI-Powered Recommendation System What You'll Build Building content recommendations with embeddings. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**:

Build a RAG Chatbot in 30 Minutes What You'll Build Quick tutorial building a fully functional RAG chatbot. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Diffic

AI Code Debugger Tool What You'll Build Using LLMs to automatically identify and fix bugs. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermedi

ChatGPT Plugin Development What You'll Build Creating OpenAI plugins and GPT Actions. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermediate

Multi-Agent System with CrewAI What You'll Build Orchestrating a team of specialized AI agents. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Inte

Knowledge Graph from Text What You'll Build Building knowledge graphs from unstructured documents. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: I

Multimodal Document Parser What You'll Build Extracting structured data from documents with vision. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**:

AI Data Analyst Tool What You'll Build Natural language to Python data analysis. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermediate **Pr

AI-Powered API Documentation What You'll Build Auto-generating API docs from code with LLMs. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Interme

PDF Question Answering System What You'll Build Q&A over PDF documents using LlamaIndex. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermediat

Automated Email Classifier What You'll Build Building an email classification system with LLMs. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Inte

Autonomous Research Agent What You'll Build Building agents that autonomously search and synthesize. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**:

Structured Data Extraction What You'll Build Extracting structured JSON from unstructured text. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Inte

Semantic Search with OpenAI Embeddings What You'll Build Building semantic search using text-embedding-3-large. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Di

Build a Voice AI Assistant What You'll Build Creating voice-to-voice AI assistant with Whisper and TTS. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty

Conversational AI with Memory What You'll Build Implementing persistent memory in chatbots. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermed

Real-time Transcription with AI What You'll Build Live speech-to-text and translation pipeline. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Inte

Real-time AI Streaming with FastAPI What You'll Build Server-sent events for streaming LLM responses. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**

LLM-Powered CLI Tool What You'll Build Building a command-line AI assistant in Python. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty**: Intermediate

LangChain ReAct Agent from Scratch What You'll Build Building a ReAct agent with tools using LangChain. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty

Build a Coding Interview AI What You'll Build AI system for conducting and evaluating coding interviews. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficult

Fine-tune Llama 3.2 on Your Data What You'll Build End-to-end Llama 3.2 fine-tuning on custom dataset. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty*