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LLM Engineer: Build, Fine-Tune, and Deploy Production-Grade AI Applications with Python and Modern LLMs
Paperback
Publisher: Independently Published
Published: Apr 23 2026
Pages: 260
Weight: 0.78
Height: 0.55 Width: 6.00 Depth: 9.00
Language: English
Large language models are redefining how software is built.
From intelligent assistants to automated workflows and AI driven products, LLMs are becoming a core part of modern applications. But building reliable, scalable systems with them requires more than simple API calls.
LLM Engineer is a practical, end to end guide to designing, building, fine tuning, and deploying production grade AI applications using Python and modern large language models.
This book focuses on real engineering practices that turn prototypes into scalable AI systems.
Why LLM engineering is a new discipline
Working with LLMs introduces new challenges:
- non deterministic outputs
- prompt sensitivity and variability
- latency and cost constraints
- data management and retrieval
- evaluation and quality control
- deployment and monitoring
This book teaches you how to handle these challenges with proven approaches.
What you will learn
- fundamentals of large language models
- prompt design and optimization strategies
- building applications powered by LLMs
- retrieval augmented generation systems
- fine tuning and model customization
- evaluation and benchmarking techniques
- handling hallucinations and errors
- scaling applications for real users
- cost and latency optimization
- deployment and monitoring in production
From prototype to production AI
Throughout the book, you will learn how to:
- design robust AI application architectures
- integrate LLMs into real software systems
- build pipelines for data and inference
- improve output reliability and consistency
- deploy scalable AI services
- monitor performance and iterate continuously
Each chapter focuses on practical engineering decisions.
Practical applications
- AI powered SaaS products
- intelligent chatbots and assistants
- document analysis systems
- automated content generation
- business process automation
- knowledge retrieval systems
These examples reflect real use cases in modern software.
Who this book is for
- software engineers
- AI engineers
- data scientists
- backend developers
- professionals building AI powered products
If you want to move beyond experimentation and build production ready AI applications, this book provides the roadmap.
Design intelligently.
Build reliably.
Deploy AI at scale.
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