Generative AI for Software Development

In this section

• Module-1
  Introduction to Generative AI fundamentals covering exploration of GenAI applications in SDLC LLMs NLP tokens and practical tools for software development
  • Generative AI
    This document provides a comprehensive introduction to generative AI, tracing its evolution from traditional AI to foundation models, and exploring its impact on software development and its relationship with machine learning deep learning, and LLMs.
  • Generative AI in Software Development
    This document provides a comprehensive overview of integrating generative AI into the Software Development Life Cycle, covering requirements gathering analysis, design, implementation, testing, deployment, and maintenance phases.
  • Large Language Models
    This document provides a comprehensive guide to Large Language Models covering their architecture, applications in software development, risks, and ethical considerations.
  • Natural Language Processing
    This document introduces the fundamentals of Natural Language Processing including text analysis, language understanding, machine translation sentiment analysis, and NLP applications in software development.
  • Tokens in Generative AI
    This document provides a comprehensive guide to tokens in generative AI covering tokenization, text processing, input limits, token pricing, and optimization strategies for AI models.
  • AI Help in Best Practices and Design
    This document provides guidance on leveraging AI for software development best practices, design patterns, code review, optimization, and architectural recommendations to improve development workflows.
  • Database Design Assignment
    This document presents a practical assignment on designing a relational database using AI tools, covering schema creation, normalization, query optimization, and best practices for relational database development.
  • Static Site Development
    This document provides a guide to static site development using AI tools covering site generators, content management, deployment strategies, and optimization techniques for modern web development.
  • Design Diagrams
    This document provides a comprehensive guide to creating design diagrams using AI tools, covering system architecture, UML diagrams, flowcharts, and visual documentation for software development projects.
  • GenAI and Design Tools
    This document provides an overview of generative AI and design tools for software development, covering popular platforms, features, integrations, and best practices for enhanced productivity.
  • Legacy Code
    This document provides a guide to managing legacy code using AI tools covering code analysis, modernization, refactoring, documentation, and migration strategies for legacy systems.
  • Prompts in SDLC
    This document provides a comprehensive guide to using prompts in the Software Development Life Cycle, covering prompt engineering, best practices, AI integration, and optimization strategies for development workflows.
  • Generative AI Module Summary
    This document summarizes the generative AI module, covering key concepts including AI fundamentals, LLMs, NLP, tokens, practical applications, and tools for software development.
• Module-2
  Advanced generative AI applications covering practical implementation AI integration strategies deployment considerations and real-world case studies for software development
  • CI/CD Automation
    This document provides an overview of AI integration in CI/CD pipelines focusing on automated testing, code optimization, intelligent release orchestration, and AI-enabled DevOps tools.
  • AI Tools for Security in Software Development
    This document explores the integration of AI tools in software development security, covering automated code reviews, threat detection, machine learning applications, and preventive cybersecurity measures.
  • AI in Software Testing
    This document explores generative AI applications in software testing including machine learning, NLP, and intelligent automation techniques for improved test efficiency and coverage.
  • Generating Test Case
    This document demonstrates the use of AI to generate comprehensive test cases for software modules, with examples of prompt engineering for user registration validation scenarios.
  • Generative AI in Software Development
    Comprehensive guide to integrating generative AI in software development workflows, covering AI-powered code review, debugging, documentation generation, education, training tools, and practical exercises for automation and enhanced productivity.
  • AI Considerations in Software Development
    Comprehensive guide to AI considerations in software development, covering ethics, fairness, explainability, robustness, transparency, privacy intellectual property, security, compliance, and bias mitigation for responsible AI implementation.
  • Innovation with Generative AI
    Exploration of integrating AI-powered features into mobile applications for enhanced user experiences and innovative functionalities, particularly in photo memory applications.
  • Module Summary and Cheatsheet
    Comprehensive summary and cheatsheet covering generative AI integration in software development, including DevOps automation, security enhancement threat detection, platforms, secure coding tools, AI-powered debugging documentation and career opportunities.
  • Final Assignment
    Final assignment project to create CodeCraftHub personalized learning platform using generative AI, ChatGPT, Node.js, MongoDB, Express.js, with requirements gathering, database design, code generation, testing, and Docker deployment.