Generative AI for Software Developers : Future-Proof Your Career with AI-powered Development and Hands-on Skills.

Format:

Book

Author/Creator:

Shrivastava, Saurabh.

Contributor:

Arora, Kamal.

Dubey, Ashutosh.

Thakur, Dhiraj.

Sahay, Sanjeet.

Language:

English

Physical Description:

1 online resource (454 pages)

Edition:

1st ed.

Place of Publication:

Birmingham, UK : Packt Publishing Ltd., 2025.

Birmingham : Packt Publishing, Limited, 2025.

Summary:

Master Generative AI in software development with hands-on guidance, from coding and debugging to testing and deployment, using GitHub Copilot, Amazon Q Developer, and OpenAI APIs to build scalable, AI-powered applications Key Features Hands-on guidance for mastering AI-powered coding, debugging, and deployment with real-world examples.

Contents:

Cover

Title page

Copyright page

Foreword

About the Authors

About the Reviewers andContributors

Table of Contents

Chapter 1 - The Art and Science of Generative AI

What is Generative AI?

Generative AI Vocabulary

Understanding the Hierarchy: From AI to Generative AI

Current Landscape and Future Prospects of Generative AI

Generative AI Use Cases

Key Categories of Generative AI Use Cases

User Experience Transformation

Improving Workforce Productivity

Business Operations Efficiency

Generative AI Use Cases Across Industries

Banking and Financial Services

Healthcare and Life Sciences

Media and Entertainment

Manufacturing and Automotive

Retail and E-commerce

Generative AI Benefits

Improved Productivity and Decision-Making

Enhanced Creativity and Innovation

Personalized Customer Experience

Automation and Cost Reduction

Breaking New Scientific Barriers

Boosting Software Development Productivity

Myths Around Generative AI

Myth: Generative AI is Too New, So We Should Wait

Myth: Generative AI Will Replace Human Jobs Entirely

Myth: Generative AI is Dangerous

Myth: Generative AI is the Solution to All Problems

Myth: A Single Model Can Be Standardized for All Use Cases

Challenges of Generative AI

Data Quality and Bias

Ethical and Regulatory Concerns

Privacy and Security Risks

Accuracy and Reliability

Intellectual Property Concerns

Generative AI for Software Development

How Developers Should Evolve with GenAI

Summary

Chapter 2 - Getting Started with Generative AI

Expanding Your Generative AI Knowledge: SLMs, LLMs, and LMMs

Small Language Models (SLMs)

Large Language Models (LLMs)

Large Multimodal Models (LMMs)

Foundation Models in Generative AI

GPT-4 (OpenAI)

API Access

Key Differentiating Capabilities.

Claude (Anthropic)

Integration and APIs

Key Differentiating Features

Performance Highlights

Gemini (Google)

Gemini Model Variants

Integration and Access

Llama (Meta)

Integration Mechanisms

Titan and Nova (Amazon)

Integration Capabilities

Amazon Nova Family

Cohere Models

Key Differentiators

How to Start with Generative AI

GenAI Chatbots

Step 1: Qualify the Use Case-Does It Require a Chatbot?

Step 2: Clearly Articulate the Purpose and Success Criteria

Step 3: Identify the Right Platform and Provider

Step 4: Define the Chatbot Architecture

Step-5: Setup API/SDK Connections

Step-6: Define Prompt Management Strategy

Step-7: Add Guardrails

Step-8: Build a RAG-Based Knowledge Base and Context Management

Step-9: Deploy Your Prompts and User Interface

Step-10: Monitor, Improve, Iterate, and Re-deploy

Code Generation Using Generative AI

Step 1: Install Amazon Q Developer Plugin

Step 2: Configure AWS Builder ID

Step 3: Generate Code

Step 4: Improve the Code

Step 5: Add Inline Documentation

Step 6: Explain Specific Functions

Agentic AI Workflows

Foundational patterns of agentic AI

How Generative AI is Becoming Democratized

Chapter 3 - Generative AI Architecture Fundamentals

Understanding Generative AI Models Architecture

Benefits of Understanding Generative AI Models Architecture

Category of Generative AI Models and Their Architecture

Generative Adversarial Networks (GANs)

Variational Autoencoders (VAEs)

Transformer-based Models

Approaches of Generative Models

Explicit Density Models

Implicit Density Models

Autoregressive Models

Hyperparameter Tuning and Regularization.

Hyperparameter Tuning in GenAI

Regularization in GenAI

Practical Benefits for Software Development

Model Evaluation Techniques

Quantitative Metrics

Qualitative and Human Evaluation Methods

Specialized Evaluation Techniques

Choosing the Right Generative Model for Specific Use Cases

Model Selection Based on Application Requirements

Practical Considerations for Software Development

Best Practices for Model Evaluation

Importance of Continuous Monitoring and Evaluation

Chapter 4 - Generative AI in Software Development

Impact of Generative AI on Software Development

Acceleration of Development Processes:

Enhanced Code Quality and Consistency:

Automated Testing and Debugging:

Intelligent Documentation and Knowledge Sharing:

Bridging the Gap Between Technical and Non-Tech Stakeholders:

Enabling Innovation and Creativity:

Scaling Development to Meet Growing Demands:

Data-Driven Development Decisions:

Essential Tools and Frameworks for Gen AI-Based Software Application Development

Foundation Models (FMs)

Development Frameworks

LangChain

Transformers (Hugging Face)

BentoML

As shown in the diagram below, if you need to deploy a fine-tuned GPT model as a cloud-based API for summarizing documents, use BentoML to package the model with its dependencies and deploy it on AWS Lambda.

Comparative Overview of GenAI Development Framework

Cloud-Based GenAI App Development Framework

Amazon Bedrock

Google Cloud Vertex AI Vertex AI is Google Cloud's unified AI platform, providing pre-trained and fine-tuned models, including their state-of-the-art PaLM (Pathways Language Model) family. Vertex AI simplifies ML workflows by integrating data preproce...

Microsoft Azure OpenAI Service

IBM Watson AI.

IBM Watson AI provides API-based access to models for NLP, language understanding, and conversational AI. It focuses on enterprise use cases, offering models optimized for business-specific workflows. Watson AI Key Features include:

Ten Reasons to Use Cloud-Based GenAI Development Framework

Generative AI Code Generation Tools

Poolside

OpenAI Codex

Replit Ghostwriter

Gen AI-Assisted Integrated Development Environments

Visual Studio Code with AI Extensions

JetBrains IDEs with AI Features

Replit

Microsoft Visual Studio with IntelliCode

AWS Cloud9

UI/UX Tools for Generative AI Applications

Gradio

Streamlit

Streamlit is a popular framework for building data-driven and machine-learning web applications with Python. It focuses on simplicity and enables rapid prototyping of interactive dashboards and tools. Streamlit is ideal for building web apps that show...

Flask/Django (with Front-End Libraries)

Shiny for Python/R

LangChain.js

Data Storage and Vector Databases in GenAI Applications

Traditional Data Storage

Vector Databases

Hybrid Systems

Vector DB Integration in GenAI Workflows

Version Control Tools for Generative AI App Development

GenAI App Monitoring and Observability Tools

Testing and Quality Assurance in GenAI Applications

GenAI Ops: Operationalizing Generative AI Applications

Chapter 5 - Prompt Engineering For Software Developers

Why Prompt Engineering?

Anatomy of a Prompt

Top 5 Reasons to Learn Prompt Engineering

Prompt Techniques

Zero-Shot Prompting

Few-Shot Prompting

Chain of Thought (CoT) Prompting

Self-Consistency

How Self-Consistency Works

ReAct: Reasoning and Acting Prompting Technique

Retrieval Augmented Generation (RAG)

Prompt Use Cases for the Software Development Lifecycle (SDLC)

Code Debugging.

Software Design Documentation

UML Diagram Generation

Test Case Generation

Deployment Script Generation

Prompt Management Cycle and Best Practices

Prompt Design

Prompt Testing and Evaluation

Prompt Refinement

Prompt Versioning and Cataloging

Prompt Deployment

Monitoring and Iteration

Prompt Engineering Tools

Prompt Writing Tools

Prompt Templates

Prompt Storing, Versioning, and Sharing Tools

Debugging and Optimization Tools

End-to-End Prompt Management Platforms

Chapter 6 - Integrating Generative AI into the Software Development Cycle

Industry Study on Developer Productivity with Generative AI

Transforming Software Development with Generative AI in the SDLC

Planning and Task Management

Coding and Development

Testing and Quality Assurance

CI/CD and AI-Powered Automation

Generative AI for Specific Programming Tasks

Code Generation

Code Completion

Bug Detection and Debugging

Code Refactoring and Optimization

Code Documentation

Automated Test Case

Code Reviews

Learning and Teaching Programming

End-to-End AI Integration in the SDLC

Challenges and Tradeoffs in AI Integration

Balancing AI Model Updates and Infrastructure Costs

Maintaining Developer Control Over AI-Generated Code

Ensuring Security and Compliance in AI-Generated Code

Key Metrics and KPIs for Measuring AI Impact

Next Steps: Sustaining and Expanding AI Integration

Pilot AI Tools in Targeted Development Stages

Continuous Model Training and Feedback Loops

Establish Success Metrics and Iterate

The Future Outlook

Chapter 7 - Ethical and Security Best Practices in Generative AI

Why the New Concerns?

Bias in AI-Generated Code

Training Data Bias

Underrepresentation of Certain Groups

Lack of Diversity in Coding Styles.

Model Architecture and Optimization Bias.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-80667-118-2

9781806671182

1-80667-119-0

OCLC:

1545643748