AI-Assisted Software Development Training Course
AI-Assisted Software Development Training Course is designed to empower software professionals with Artificial Intelligence (AI), Generative AI, Large Language Models (LLMs), AI-powered coding assistants, intelligent automation, and modern software engineering practices.
Course Overview
AI-Assisted Software Development Training Course
Introduction
AI-Assisted Software Development Training Course is designed to empower software professionals with Artificial Intelligence (AI), Generative AI, Large Language Models (LLMs), AI-powered coding assistants, intelligent automation, and modern software engineering practices. The course focuses on transforming traditional development workflows through AI-enhanced coding, automated testing, intelligent debugging, code optimization, prompt engineering, and AI-driven software lifecycle management. Participants learn how to leverage advanced AI tools to accelerate application delivery, improve code quality, enhance developer productivity, and build scalable digital solutions aligned with modern enterprise needs.
This comprehensive training combines software engineering excellence with AI innovation, enabling developers and technology teams to adopt AI-first development strategies, autonomous coding workflows, DevOps automation, cloud-native application development, and responsible AI practices. Through practical labs, real-world projects, and industry case studies, learners gain hands-on experience applying AI throughout the Software Development Life Cycle (SDLC), from requirements analysis and architecture design to deployment, monitoring, and continuous improvement.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Master AI-assisted software engineering workflows using modern AI development tools.
- Apply Generative AI and Large Language Models (LLMs) for software creation and automation.
- Develop effective prompt engineering strategies for coding and technical problem-solving.
- Use AI-powered tools for code generation, refactoring, optimization, and documentation.
- Implement AI-driven software testing and quality assurance automation.
- Improve developer productivity through AI-enhanced Integrated Development Environments (IDEs).
- Build applications using AI-native development methodologies.
- Apply responsible AI, security, governance, and ethical coding practices.
- Integrate AI assistants into Agile, DevOps, and CI/CD pipelines.
- Use AI for debugging, vulnerability detection, and performance improvement.
- Design scalable solutions using cloud-native and AI-powered architectures.
- Evaluate AI development tools based on business and technical requirements.
- Create future-ready software solutions using human-AI collaboration models.
Target Audience
- Software Developers and Programmers
- Full-Stack Application Developers
- Software Architects and Technical Leads
- DevOps and Cloud Engineers
- Quality Assurance Engineers and Test Automation Specialists
- Data Engineers and AI Engineers
- Product Managers and Technical Project Managers
- Technology Consultants and Digital Transformation Teams
Course Modules
Module 1: Introduction to AI-Assisted Software Development
- Evolution from traditional software engineering to AI-assisted development.
- Understanding Generative AI, LLMs, and AI coding assistants.
- AI impact on modern Software Development Life Cycle (SDLC).
- Developer productivity enhancement using AI technologies.
- Building an AI-first engineering mindset.
- Case Study: Microsoft GitHub Copilot Adoption
Module 2: Generative AI and Large Language Models for Developers
- Fundamentals of Generative AI models and LLM architecture.
- Using AI models for code generation and explanation.
- Selecting appropriate AI models for development tasks.
- Working with conversational AI programming assistants.
- Understanding limitations, hallucinations, and validation techniques.
- Case Study: Enterprise LLM Coding Assistant Implementation
Module 3: Prompt Engineering for Software Developers
- Designing effective developer prompts.
- Advanced prompt patterns for coding tasks.
- Prompt optimization for debugging and automation.
- Context engineering for better AI responses.
- Building reusable AI development workflows.
- Case Study: AI-Powered Code Review Assistant
Module 4: AI-Assisted Coding and Application Development
- AI-supported programming with modern IDE extensions.
- Automated code generation and completion.
- AI-assisted API development.
- Code transformation and modernization.
- Improving productivity through AI pair programming.
- Case Study: Legacy Application Modernization Project
Module 5: AI-Driven Testing and Quality Engineering
- Automated test case generation using AI.
- AI-assisted unit testing and integration testing.
- Intelligent bug detection and debugging.
- AI-powered code quality analysis.
- Predictive software defect management.
- Case Study: AI Testing Automation Platform
Module 6: AI Integration with DevOps and Cloud Development
- AI-enhanced CI/CD pipelines.
- Intelligent DevOps automation.
- AI monitoring and incident management.
- Cloud-native AI development workflows.
- Automated deployment optimization.
- Case Study: AI-Enabled DevOps Transformation
Module 7: Secure and Responsible AI Software Development
- AI-assisted cybersecurity practices.
- Secure coding with AI tools.
- Managing AI-generated code risks.
- Responsible AI principles and governance.
- Privacy-aware AI development practices.
- Case Study: Secure AI Application Development Framework
Module 8: Future of AI-Native Software Engineering
- Autonomous software development agents.
- AI-powered software architecture design.
- Multi-agent development workflows.
- Human-AI collaboration models.
- Emerging trends in AI engineering.
- Case Study: AI Software Engineering Transformation Strategy
Training Methodology
- Interactive lectures and presentations.
- Group discussions and brainstorming sessions.
- Hands-on exercises using real-world datasets.
- Role-playing and scenario-based simulations.
- Analysis of case studies to bridge theory and practice.
- Peer-to-peer learning and networking.
- Expert-led Q&A sessions.
- Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.