From Frontend to AI Engineering

Most AI development content assumes Python. If you're a TypeScript developer who's tried to get started with AI — and kept hitting a wall of Python libraries, Python tutorials, and Python examples — this book is for you.

The core insight behind it: for most real products, AI development is fundamentally about calling APIs. Calling HTTP endpoints, handling async streams, validating structured responses — that's exactly what TypeScript developers already do. The switching cost is nearly zero. TypeScript's type system is actually a genuine advantage: Zod pairs naturally with LLM structured output, and sharing types between frontend and backend removes friction across the full stack.

This book takes you from your first LLM API call to a deployable, production-ready AI workbench — covering agents, RAG, MCP servers, and multimodal AI, all in TypeScript.

What you'll build:

• A production multi-turn conversation system with context compression and cost tracking
• A hybrid RAG pipeline (BM25 + vector search + reranking) backed by PostgreSQL and pgvector
• A multi-tenant knowledge base where each customer's data stays strictly isolated
• A ReAct agent that reasons over tool results across multiple steps
• A multi-agent system that decomposes goals, executes subtasks in parallel, and synthesizes results
• An autonomous coding agent that writes, runs, and self-repairs code in a sandbox
• An MCP server you can publish to npm and reuse across any MCP-compatible host
• A complete AI workbench integrating RAG, agents, MCP, and multimodal capabilities — deployable to Vercel and Railway

What's covered:

The book is organized into six parts. It starts with full-stack TypeScript foundations — the server patterns and type-safe API design you'll need before touching any LLM. Then it moves into LLM integration: streaming output, prompt engineering, and reliable structured output with Zod.

The RAG section covers the full pipeline from document ingestion to production retrieval — including hybrid search, hallucination detection, and multi-tenant isolation. The Agents section is the heart of the book: from the ReAct loop through tool calling, browser automation, and multi-agent orchestration.

Part Five covers MCP — Anthropic's Model Context Protocol — from building your first server through publishing a reusable tool package to npm. Part Six covers multimodal AI (vision, speech, documents) and the production concerns you can't skip before going live: observability with LangFuse, rate limiting, prompt injection defense, and cost monitoring.

Who this is for:

TypeScript or JavaScript developers who want to build AI applications. You should be comfortable with async/await and basic Node.js. You don't need Python, and you don't need a machine learning background. This is not a research book — it's an engineering book.

If you've been watching AI development from the sidelines because the ecosystem felt like it belonged to Python developers, this book is your on-ramp.

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**600+ pages. 24 chapters. All code in TypeScript.**