Generative AI with JavaScript & TypeScript: A Practical Developer's Guide to LLMs, Prompt Engineering, RAG, AI Agents, Tool Calling, LangChain, LangGraph, MCP, and Production AI Applications

Pandey, Garima

ISBN 13: 9798194609130

Published by Independently published, 2026

About this Item

Print on Demand.

Seller Inventory # I-9798194609130

Bibliographic Details

• Title: Generative AI with JavaScript & TypeScript: A Practical Developer's Guide to LLMs, Prompt Engineering, RAG, AI Agents, Tool Calling, LangChain, LangGraph, MCP, and Production AI Applications
• Publisher: Independently published
• Publication Date: 2026
• Language: English
• ISBN 13: 9798194609130
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Unlock the Power of Generative AI Using Your Existing JavaScript & TypeScript Skills!

If you are a JavaScript or TypeScript developer, you don't need to learn Python or machine learning mathematics to build modern AI applications. Generative AI with JavaScript & TypeScript delivers a clear, pragmatic engineering roadmap for software developers who want to move past simple API demos and build production-ready AI features.

Authored by experienced software developer Garima Pandey, this book guides you step-by-step through building 10 real-world projects—from streaming API clients and Zod-validated structured outputs to vector database RAG pipelines, stateful LangGraph agent workflows, and Model Context Protocol (MCP) tool integrations.

What You Will Learn:

• LLM Fundamentals: Tokenization, context windows, sampling parameters (temperature, top_p), and message roles.
• Type-Safe AI Applications: Building resilient Node.js & TypeScript integrations with Zod schemas and retry handlers.
• Prompt & Context Engineering: Grounding AI responses, decomposing prompts, and defending against prompt injection.
• Real-Time Streaming & Memory: Server-Sent Events (SSE) chatbots, sliding window sessions, and database-backed persistent user memory.
• Tool Calling: Giving AI models the ability to execute application functions and external APIs safely.
• Retrieval-Augmented Generation (RAG): Building vector search pipelines with Pinecone, parent-child chunking, hybrid search, and re-ranking.
• AI Agents & Workflows: ReAct agent loops, LangChain.js, and stateful cyclic graphs with LangGraph.js and human-in-the-loop approvals.
• Model Context Protocol (MCP): Interoperating with standardized MCP tool servers using @modelcontextprotocol/sdk.
• Production Engineering: Golden dataset evaluations, LangSmith telemetry, token cost optimization, and OWASP AI Security guardrails.

Stop learning AI as a collection of buzzwords. Learn how to engineer useful, production-ready AI applications today!