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AI with JavaScript & TypeScript: Master Your Data. Production RAG, Vector Databases, and Enterprise Search.
Are you a JavaScript developer ready to build the next generation of intelligent web applications? This book series is your hands-on guide to creating production-grade Retrieval-Augmented Generation (RAG) applications using TypeScript, Node.js, and Next.js. Move beyond abstract theory and master the entire AI pipeline, from your first LLM API call to deploying scalable, trustworthy systems. Stop just using AI tools—start building them and become a full-stack AI developer.
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About
About the Book
Are you a JavaScript developer ready to build the next generation of intelligent web applications? Your journey starts here.
The world of web development is undergoing a revolution. Static, predictable interfaces are being replaced by dynamic, conversational experiences powered by Large Language Models (LLMs). But for many JavaScript developers, the world of AI feels like a black box, locked away in Python libraries and academic papers. This book series breaks down that barrier.
Written from a web developer's perspective, The AI-Powered RAG Stack is your definitive, hands-on guide to building production-grade Retrieval-Augmented Generation (RAG) applications using the tools you already know and love: TypeScript, Node.js, Next.js, and the Vercel AI SDK.
This comprehensive series is your roadmap from beginner to expert, teaching you not just the "how" but the critical "why" behind the architecture of modern AI systems. Forget abstract theory—this is about building real, scalable, and trustworthy applications.
Across this series, you will master:
- Volume 1: Foundations: Start from scratch. Make your first LLM API call, master prompt engineering, and build your first simple Q&A chatbot with streaming responses.
- Volume 2: Agents & Tool Calling: Teach your AI to interact with the world. Build intelligent agents that can call APIs, execute functions, and make decisions to solve complex problems.
- Volume 3: Master Your Data: Dive deep into the RAG pipeline. Learn to parse, chunk, and embed documents (PDFs, HTML, Notion) into a vector database like Pinecone or pgvector. Implement advanced retrieval strategies like Hybrid Search, Re-ranking, and HyDE to ensure your AI has the perfect context, every time.
- Production-Ready Patterns: Tackle real-world challenges like caching embeddings, managing context windows, implementing feedback loops, and building hallucination guardrails to create robust, enterprise-grade systems.
Packed with practical code examples, architectural diagrams, and battle-tested "common pitfalls" sections, this series is more than a tutorial—it's a masterclass in AI engineering for the modern web developer.
Stop just using AI tools. Start building them. Your journey to becoming a full-stack AI developer begins now.
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Author
About the Author
Edgar Milvus
A veteran software engineer with 20 years of experience, I have dedicated my career to the art of automation. My philosophy is simple: programming should eliminate repetitive chores to unlock human creativity. This journey began early on with the development of custom code-generation tools and has evolved into a deep mastery of LLMs and their APIs. Today, I specialize in architecting AI-driven solutions that handle everything from complex coding and security tasks to advanced knowledge retrieval, transforming the way we interact with technology
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