AI Code Generation and Engineering Training Course
AI Code Generation and Engineering Training Course is designed to equip software professionals with advanced skills in Artificial Intelligence (AI)-powered software development, Generative AI coding, Large Language Models (LLMs), automated programming, intelligent code completion, prompt engineering, AI-assisted debugging, and modern software engineering practices.
Course Overview
AI Code Generation and Engineering Training Course
Introduction
AI Code Generation and Engineering Training Course is designed to equip software professionals with advanced skills in Artificial Intelligence (AI)-powered software development, Generative AI coding, Large Language Models (LLMs), automated programming, intelligent code completion, prompt engineering, AI-assisted debugging, and modern software engineering practices. As organizations accelerate digital transformation, AI-driven development tools are reshaping how applications are designed, developed, tested, secured, and maintained. This course provides hands-on expertise in leveraging AI coding assistants, autonomous development workflows, machine learning models for programming, and intelligent automation to improve developer productivity, code quality, and engineering efficiency.
Participants will explore the complete lifecycle of AI-enhanced software engineering, including requirements analysis, architecture design, code generation, refactoring, testing automation, DevOps integration, and responsible AI development. Through practical labs, real-world projects, and industry case studies, learners will master emerging technologies such as AI pair programming, Retrieval-Augmented Generation (RAG), AI agents, code intelligence platforms, automated software delivery, and secure AI development frameworks. The course prepares developers, architects, and technology leaders to build scalable, reliable, and innovative software solutions in an AI-first engineering environment.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Understand the foundations of AI-powered software engineering and Generative AI development workflows.
- Apply Large Language Models (LLMs) for automated code generation and software creation.
- Master prompt engineering techniques for accurate AI-assisted programming.
- Develop applications using AI coding assistants and intelligent developer tools.
- Implement AI-driven debugging, testing, and code optimization strategies.
- Build efficient workflows using AI pair programming and autonomous coding agents.
- Integrate AI models into modern software development lifecycle (SDLC) processes.
- Apply Machine Learning and Natural Language Processing (NLP) concepts in coding automation.
- Design secure applications using Responsible AI and AI governance practices.
- Implement Retrieval-Augmented Generation (RAG) solutions for enterprise coding systems.
- Automate DevOps processes using AI-powered CI/CD engineering approaches.
- Evaluate AI-generated code for quality, performance, and security compliance.
- Develop innovative software solutions using next-generation AI engineering practices.
Target Audience
- Software Developers and Application Engineers
- Full Stack Developers
- Backend and Frontend Engineers
- DevOps and Cloud Engineers
- Software Architects and Technical Leads
- AI and Machine Learning Engineers
- IT Managers and Digital Transformation Leaders
- Technology Consultants and Innovation Teams
Course Modules
Module 1: Foundations of AI Code Generation
- Introduction to Generative AI and AI-driven software development
- Evolution from traditional programming to AI-assisted engineering
- Understanding Large Language Models (LLMs) for coding applications
- Overview of AI coding ecosystems and developer platforms
- Case Study: How modern enterprises use AI assistants to accelerate software delivery
Module 2: Prompt Engineering for Software Developers
- Designing effective prompts for code generation tasks
- Advanced prompting techniques for debugging and optimization
- Context management for AI programming assistants
- Creating reusable developer prompt libraries
- Case Study: Improving developer productivity using structured AI prompts
Module 3: AI Coding Assistants and Developer Tools
- Working with AI-powered code completion platforms
- Intelligent code suggestions and automated documentation
- AI-assisted refactoring and modernization techniques
- Integrating AI tools into IDE development environments
- Case Study: Migration of legacy applications using AI coding assistants
Module 4: Automated Code Generation Techniques
- Generating applications from natural language requirements
- AI-based API, database, and interface generation
- Creating reusable software components with AI
- Understanding limitations and validation of generated code
- Case Study: Building a business application prototype using AI-generated code
Module 5: AI-Based Testing and Code Quality Engineering
- Automated test generation using Artificial Intelligence
- AI-powered bug detection and vulnerability analysis
- Intelligent code review and quality assessment
- Performance optimization using AI recommendations
- Case Study: Reducing software defects through AI-driven testing automation
Module 6: AI Agents and Autonomous Software Engineering
- Understanding AI autonomous coding agents
- Building agent-based development workflows
- Multi-agent collaboration for software projects
- AI-driven task planning and execution
- Case Study: Autonomous AI agents managing software development tasks
Module 7: Integrating AI into DevOps and Cloud Engineering
- AI-enabled CI/CD pipeline automation
- Intelligent monitoring and incident management
- AI-powered cloud application development
- Infrastructure automation using AI engineering tools
- Case Study: Implementing AI-enhanced DevOps workflows for cloud platforms
Module 8: Advanced AI Engineering and Future Development
- Building enterprise AI coding platforms
- Retrieval-Augmented Generation (RAG) for software systems
- Secure and responsible AI coding practices
- Future trends in AI-native software engineering
- Case Study: Designing an AI-first software engineering platform
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.