[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzkzMDg","title","AI Engineering with TypeScript","state","published","contactAuthorMethod","email","aboutText","

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, with Best Practices.

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 — 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.

---

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

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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