Name: Prompt Engineering in AI-Driven CRUD Development: Efficiency, Code Quality, and Security with Large Language Models (en Inglés)
SKU: 66849689
Price: 56.22 USD
Availability: InStock
Author: Shanuka, Ashen
ISBN: 9789999333931

portada Prompt Engineering in AI-Driven CRUD Development: Efficiency, Code Quality, and Security with Large Language Models (en Inglés)

Prompt Engineering in AI-Driven CRUD Development: Efficiency, Code Quality, and Security with Large Language Models (en Inglés)

Formato

Libro Físico

Editorial

Eliva Press

Autor

Shanuka, Ashen

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x0.3 cm

ISBN13

9789999333931

Prompt Engineering in AI-Driven CRUD Development: Efficiency, Code Quality, and Security with Large Language Models (en Inglés)

Shanuka, Ashen (Autor) · Eliva Press · Tapa Blanda

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Reseña del libro "Prompt Engineering in AI-Driven CRUD Development: Efficiency, Code Quality, and Security with Large Language Models (en Inglés)"

As generative artificial intelligence reshapes modern software development, prompt engineering has emerged as a critical skill for maximizing the effectiveness of large language models (LLMs) such as ChatGPT. This book presents a comprehensive and systematic exploration of how prompt engineering influences efficiency, code quality, and security in CRUD (Create, Read, Update, Delete) application development. Drawing on a rigorous systematic literature review of 52 peer-reviewed studies published between 2018 and 2023, the author evaluates the role of AI-assisted coding in automating repetitive tasks, accelerating development cycles, and improving code readability and maintainability. The book critically examines advanced prompting techniques including role-prompting, chain-of-thought prompting, and iterative refinement and their measurable impact on AI-generated code. Beyond productivity gains, the book addresses a crucial and often overlooked dimension: security. It analyzes common error patterns and vulnerabilities in AI-generated CRUD code, highlighting the risks of hallucinations, limited self-verification, and over-reliance on automated outputs. Through evidence-based discussion, the author emphasizes the necessity of continuous human oversight and best-practice security validation. Positioned at the intersection of software engineering and applied artificial intelligence, this work provides valuable insights for software engineers, researchers, educators, and technology leaders. It offers a balanced perspective on human-AI collaboration, demonstrating how developers can responsibly integrate generative AI tools to enhance not replace human expertise in modern application development.