COURSE OUTLINE

DAY 1 | AI Fundamentals & AI-Driven Software Development

·      Module 1 — Introduction to AI in Software Development

o  AI, machine learning, generative AI and large language models

o  Capabilities, limitations and common enterprise use cases

o  Where AI fits in software engineering and existing applications

o  Selecting practical opportunities for AI adoption

·      Module 2 — AI-Assisted Software Development & Productivity

o  AI-supported coding, refactoring and debugging

o  Code review, technical documentation and requirements analysis

o  Using AI throughout the software development lifecycle

o  Reviewing and validating AI-generated code

·      Module 3 — Prompt Engineering for Development Teams

o  Writing clear prompts with context, constraints and examples

o  Prompts for code generation, architecture and API specifications

o  Iterative prompting to improve development outputs

o  Recognizing incorrect or incomplete AI responses

·      Module 4 — AI-Enabled Software Design & Architecture

o  Basic patterns for AI-enabled applications

o  Connecting existing applications to AI services

o  Introduction to model APIs and cloud AI options

o  Security, maintainability and reliability considerations

DAY 2 | Intelligent Applications & Automation

·      Module 5 — Introduction to AI Application Development

o  AI-enabled application components and request-response flow

o  Connecting a basic application to an AI model using an API

o  Processing user input and displaying model responses

o  Basic input validation, failure handling and output checks

·      Module 6 — Intelligent Automation & AI Agents

o  Differences between fixed automation and AI-assisted workflows

o  Fundamentals of AI agents, tools and human approval

o  Integrating AI into business and development processes

o  Designing a simple supervised automation workflow

·      Module 7 — AI-Driven Testing & Quality Assurance

o  AI-assisted test-case and test-data generation

o  Debugging and code-review support

o  Checking generated code for correctness and security

o  Practical quality-control and regression-testing approaches

·      Module 8 — Responsible AI & Enterprise Adoption

o  Privacy, confidentiality and safe handling of enterprise data

o  Intellectual property and AI-generated code considerations

o  Model limitations, human review and governance

o  Identifying pilot use cases and practical productivity measures