LLM Engineering for Developers: From Prototype to Production is a practical guide to designing, developing, deploying, and evaluating Large Language Model (LLM) applications for real-world use cases in India. It bridges the gap between AI fundamentals and production-ready engineering, helping developers build cost-effective, scalable, and multilingual AI solutions.

The book introduces the LLM ecosystem from an Indian perspective, covering model architectures, tokenization, prompt engineering, embeddings, semantic search, Retrieval-Augmented Generation (RAG), and fine-tuning. It explores tools and frameworks such as Hugging Face, Ollama, ChromaDB, FAISS, LangGraph, CrewAI, FastAPI, and Docker, with an emphasis on practical implementation and budget-conscious development.

Special attention is given to India's multilingual environment, including Hindi, Hinglish, regional languages, speech technologies, and Indian datasets. Through code examples and project-based learning, readers explore applications such as regulatory document assistants, NCERT question-answering systems, legal document search, e-commerce customer support bots, GST assistance, and educational AI platforms.

The book also addresses production deployment, API integration, cost optimization, model evaluation, hallucination detection, bias, safety guardrails, and regulatory compliance. Three capstone projects demonstrate how to develop practical AI systems for education, institutional document intelligence, and financial news analysis.

Designed for software developers, AI engineers, computer science students, researchers, and technology professionals, this book provides a structured pathway from LLM experimentation to production deployment, with examples and challenges relevant to the Indian technology ecosystem.

Dr. Nishant Munjal is a Senior Assistant Professor in the Department of Computer Science and Engineering at Gurukula Kangri (Deemed to be University), Haridwar, with 15 years of experience helping students unlock the power of problem-solving through programming and translating complex AI concepts into practical, hands-on learning. His research spans deep learning, personalized recommendation systems, and reinforcement learning, exploring hybrid models — including LSTMs, GANs, and autoencoders — that bridge theory and real-world application, with recent work specifically examining LLM hallucinations as an emerging challenge in generative AI.

A research management system, and an AI-powered LMS — giving him firsthand experience with what it actually takes to move an AI idea from a notebook experiment to something reliable enough for real users. In LLM Engineering for Indian Developers: From Prototype to Production, he brings that same practical, build-it-and-ship-it mindset to teaching developers how to go beyond calling an API — covering the engineering discipline, tooling, and production concerns needed to turn LLM prototypes into dependable, real-world applications.

Chapter 1. The LLM Landscape — An Indian Perspective

Chapter 2. Setting Up Your Environment (India-Optimized)

Chapter 3. LLM Architecture You Actually Need to Know

Chapter 4. Prompt Engineering for Indian Applications

Chapter 5. Embeddings and Semantic Search

Chapter 6. RAG Systems for Indian Knowledge Bases

Chapter 7. Fine-Tuning for Indian Domains

Chapter 8. AI Agents and Tool Calling

Chapter 9. Multilingual NLP and Speech

Chapter 10. Deploying AI on Indian Infrastructure

Chapter 11. LLM Safety, Evaluation, and Hallucination Detection

Chapter 12. Three Production-Ready Indian AI Systems