AI Prompt Engineering with LLMs for Coding

This volume examines advanced prompt engineering techniques applied to large language models in professional software development contexts. It addresses the design, refinement, and systematic evaluation of prompts that improve code generation, refactoring, debugging, architectural reasoning, and integration of generative systems into existing engineering workflows.
Readers will find detailed treatments of context management, multi-turn interaction patterns, constraint formulation, output verification strategies, and domain-specific adaptations for languages, frameworks, and toolchains commonly used in production environments. Emphasis is placed on measurable improvements in reliability, maintainability, and alignment with established software engineering practices rather than exploratory or introductory experimentation.
The material assumes prior experience with modern programming, version control, testing methodologies, and basic familiarity with transformer-based models. It is intended for software engineers, technical leads, and applied AI practitioners responsible for incorporating large language models into coding pipelines and development processes.
Acquire this reference to strengthen the precision and consistency of generative assistance within professional coding environments.