Inquire now

AI-Powered Software Development equips developers with the knowledge and practical skills to use AI for code generation, debugging, testing, documentation, code optimization, and modern software engineering throughout the software development lifecycle.

 

Duration 5 Days – 35 hrs.

 

Overview

The AI-Powered Software Development Training Course is designed to help software professionals improve productivity, accelerate development cycles, and enhance software quality using Artificial Intelligence (AI) tools and modern development practices.

This course introduces participants to AI-assisted coding, intelligent debugging, automated testing, code generation, documentation automation, AI-driven DevOps, and the practical use of Generative AI tools within the Software Development Life Cycle (SDLC). Participants will learn how to integrate AI into daily development workflows while maintaining software quality, security, governance, and best practices.

The training combines lectures, demonstrations, hands-on exercises, workshops, and mini-project activities to provide practical experience using AI-powered development platforms and tools.

 

Objectives

  • Understand the fundamentals of AI in software development
  • Use AI-powered coding assistants effectively
  • Apply AI tools for code generation and optimization
  • Improve software quality through AI-assisted testing and debugging
  • Automate documentation and development workflows using AI
  • Integrate AI into Agile and DevOps environments
  • Identify risks, limitations, and governance considerations when using AI
  • Build simple AI-assisted applications and development workflows
  • Increase productivity while maintaining coding standards and security

 

 Target Audience

  • Software Developers
  • Full-Stack Developers
  • Web Developers
  • Mobile Application Developers
  • QA Engineers and Testers
  • DevOps Engineers
  • Technical Leads
  • System Analysts
  • IT Project Managers
  • Software Architects
  • IT Professionals interested in AI-assisted development

 

Prerequisites

  • Basic knowledge of programming concepts
  • Experience in any programming language (e.g., JavaScript, Python, Java, C#, PHP)
  • Basic understanding of software development lifecycle (SDLC)
  • Familiarity with Git or version control is an advantage
  • Basic understanding of APIs and web applications is recommended

 

 

Course Outline

 

Module 1: Introduction to AI-Powered Software Development

  • Overview of Artificial Intelligence in software engineering
  • Evolution of AI-assisted development
  • Benefits and challenges of AI in development
  • Understanding Generative AI and Large Language Models (LLMs)
  • AI use cases across SDLC
  • AI trends in modern software development
  • Ethical and responsible AI usage
  • AI limitations and risks

Hands-On Activities

  • Exploring AI development tools
  • Basic AI prompt exercises

 

Module 2: AI-Assisted Coding and Development

  • Introduction to AI coding assistants
  • AI-powered code completion and suggestions
  • Code generation using AI prompts
  • Writing effective prompts for developers
  • AI-assisted refactoring techniques
  • Improving code readability and maintainability
  • AI for rapid prototyping
  • Best practices when using AI-generated code

Hands-On Activities

  • Generating code snippets
  • Refactoring existing code using AI tools
  • Creating APIs with AI assistance

 

Module 3: AI for Debugging and Software Testing

  • AI-assisted debugging techniques
  • Error analysis using AI tools
  • Automated unit test generation
  • AI-powered test case creation
  • Intelligent bug detection
  • AI in regression testing
  • Code quality analysis
  • Static and dynamic code analysis

Hands-On Activities

  • Generating unit tests
  • Debugging applications using AI
  • AI-assisted issue resolution

 

Module 4: AI in DevOps and Automation

  • AI integration in CI/CD pipelines
  • AI-powered DevOps practices
  • Infrastructure automation concepts
  • AI for deployment monitoring
  • AI-assisted log analysis
  • Predictive monitoring and alerting
  • AI in cloud-native environments
  • Automating repetitive development tasks

Hands-On Activities

  • AI-assisted pipeline generation
  • Monitoring and troubleshooting exercises

 

Module 5: AI for Documentation and Productivity

  • Automated technical documentation
  • AI-generated API documentation
  • AI for user stories and requirements gathering
  • AI-assisted Agile documentation
  • Knowledge management using AI
  • AI for project estimation and planning
  • Productivity improvement strategies
  • Collaboration with AI tools

Hands-On Activities

  • Creating technical documentation using AI
  • Generating Agile artifacts

 

 Module 6: Secure and Responsible AI Development

  • Security risks of AI-generated code
  • Secure coding practices with AI
  • AI governance and compliance
  • Data privacy and confidentiality
  • Managing AI hallucinations and inaccuracies
  • Intellectual property considerations
  • Human review and validation processes
  • AI adoption strategies for organizations

Hands-On Activities

  • Reviewing AI-generated code for vulnerabilities
  • AI governance workshop

 

Module 7: Building AI-Enhanced Applications

  • Integrating AI APIs into applications
  • Introduction to AI SDKs and frameworks
  • Chatbot and assistant integration concepts
  • AI-enhanced application architecture
  • Prompt engineering fundamentals
  • AI workflow orchestration
  • Real-world AI development scenarios
  • Future of AI-driven software engineering

Hands-On Activities

  • Building a simple AI-powered application
  • AI integration mini-project

 

Final Workshop and Capstone Activity

  • End-to-end AI-powered development workflow
  • Team-based practical exercises
  • Mini capstone project presentation
  • Best practices review
  • Open forum and consultation

Inquire now

Best selling courses

The Data Analytics with DAX, Python, SQL, and Business Intelligence Tools Training Course provides participants with practical knowledge and skills for collecting, preparing, analyzing, modeling, and visualizing data to support informed business decision-making. The course combines key data analytics technologies and techniques, including Python, SQL, DAX (Data Analysis Expressions), Power BI, data modeling, data transformation,...

The Project Management – SDLC (Software Project Management: The Complete Course) Training Course is a comprehensive program designed to equip project managers, software development leaders, business analysts, product owners, and IT professionals with the knowledge and practical skills required to successfully manage software development projects throughout the entire Software Development Life Cycle (SDLC). This course...

Warehouse Operations and Inventory Management Training Course     Duration 2 Days – 14 hrs.   Overview   This course provides a practical understanding of warehouse operations and inventory management for personnel who supervise, review, or audit warehouse processes. It covers the movement of goods from receiving through dispatch, with emphasis on warehouse layout, storage systems, picking strategies, stock rotation, inventory accuracy, and operational controls.   Participants will learn to identify process weaknesses, investigate stock discrepancies, reduce shrinkage, and interpret warehouse productivity indicators. The course connects day-to-day warehouse activities with the records, controls, and performance measures needed for effective supervision and auditing.   Objectives  

  • Explain warehouse processes and their impact on inventory accuracy and service performance.
  • Evaluate warehouse layouts, storage systems, and product locations.
  • Identify appropriate controls for receiving, put-away, storage, picking, and dispatch.
  • Apply First-In, First-Out (FIFO) and First-Expired, First-Out (FEFO) principles.
  • Compare picking strategies based on order characteristics and operational requirements.
  • Identify causes of inventory discrepancies and recommend corrective actions.
  • Develop a cycle counting approach based on inventory value, movement, and risk.
  • Recognize sources of shrinkage and recommend preventive and detective controls.
  • Interpret warehouse productivity and inventory performance KPIs.
  • Review warehouse processes using supporting records, observations, and control evidence.
        Target Audience  
  • Warehouse supervisors, team leaders, and managers.
  • Inventory controllers and stock control personnel.
  • Logistics and distribution supervisors.
  • Internal auditors and operational compliance personnel.
  • Supply chain and operations professionals.
  • Personnel responsible for reviewing warehouse controls and performance.
  Prerequisites  
  • Basic familiarity with warehouse, logistics, or inventory activities.
  • General understanding of business processes and operational documentation.
  • Basic numerical skills, including percentages and simple calculations.
  • Basic spreadsheet skills are helpful.
  • No prior experience with a specific Warehouse Management System (WMS) is required.
   

Course Outline

  Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls  
  • Warehouse roles within the supply chain.
  • End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
  • Physical stock movement and corresponding system transactions.
  • Responsibilities, authorization, and segregation of duties.
  • Common operational weaknesses and audit evidence.
  Module 2: Warehouse Layout and Storage Systems  
  • Functional zones, movement paths, and space utilization.
  • Receiving, storage, picking, staging, and dispatch areas.
  • Storage options: floor stacking, shelving, pallet racking, and bin storage.
  • Fixed and dynamic location systems.
  • Product slotting based on movement, size, handling needs, and compatibility.
  • Layout bottlenecks and their effects on productivity and inventory control.
  Module 3: Receiving and Put-Away  
  • Purchase orders, delivery documents, and expected receipts.
  • Quantity, condition, and item identification checks.
  • Recording shortages, overages, damage, and discrepancies.
  • Controls for acceptance, quarantine, and release.
  • Put-away rules and location confirmation.
  • Traceability through labels, barcodes, lot numbers, and serial numbers.
  • Timely recording of receipts and stock movements.
  Module 4: Storage, Stock Rotation, and Replenishment  
  • Storage discipline and location integrity.
  • FIFO and FEFO principles, applications, and limitations.
  • Batch, lot, and expiry-date management.
  • Controls for slow-moving, obsolete, damaged, and expired stock.
  • Replenishment of picking locations.
  • Supervision and review of stock rotation compliance.
    Day 2: Order Fulfillment and Inventory Accuracy Module 5: Picking Strategies and Order Fulfillment  
  • Discrete, batch, zone, and wave picking.
  • Selecting picking strategies for different order profiles.
  • Pick paths, location sequencing, and travel reduction.
  • Replenishment coordination and stock availability.
  • Picking verification and error prevention.
  • Packing, staging, and dispatch confirmation.
  • Handling short picks, substitutions, cancellations, and returns.
  Module 6: Inventory Records and Accuracy Controls  
  • Item master data, units of measure, and location records.
  • On-hand, available, allocated, and quarantined stock.
  • Transaction controls for receipts, transfers, issues, returns, and adjustments.
  • Common causes of physical and system stock mismatches.
  • Barcode scanning and WMS control points.
  • Inventory reconciliation and supporting documentation.
  • Measuring inventory accuracy and interpreting discrepancies.
  Module 7: Cycle Counting and Physical Inventory  
  • Cycle counting versus full physical inventory.
  • ABC classification and risk-based count priorities.
  • Count frequency based on value, movement, and discrepancy history.
  • Count preparation, blind counting, and movement controls.
  • Recounts, variance investigation, and adjustment approval.
  • Root cause analysis and corrective action follow-up.
  • Reviewing the effectiveness of the counting program.
  Module 8: Shrinkage Reduction and Inventory Protection  
  • Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
  • Access controls and accountability for stock movements.
  • Controls for high-value and sensitive inventory.
  • Returns, write-offs, scrap, and disposal controls.
  • Exception reporting and unusual transaction patterns.
  • Investigating recurring losses and unexplained adjustments.
  • Preventive and detective controls for shrinkage reduction.
    Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs  
  • Receiving productivity and dock-to-stock time.
  • Put-away productivity and replenishment performance.
  • Picking productivity and order accuracy.
  • Inventory accuracy and cycle count variance.
  • Space utilization and storage capacity.
  • Shrinkage, damage, and expiry indicators.
  • Defining KPI formulas, data sources, and reporting boundaries.
  • Interpreting trends while balancing speed, accuracy, and service.
      Module 10: Supervising and Auditing Warehouse Processes  
  • Process mapping and identification of critical control points.
  • Reviewing procedures against actual warehouse practices.
  • Tracing transactions from documents to systems and physical stock.
  • Reviewing approvals, access rights, and segregation of duties.
  • Identifying control gaps and recurring process exceptions.
  • Documenting findings with clear supporting evidence.
  • Prioritizing corrective actions and tracking closure.
  Module 11: Warehouse Improvement Planning  
  • Identifying bottlenecks, unnecessary movement, and rework.
  • Improving layout, slotting, and picking arrangements.
  • Addressing inventory discrepancy root causes.
  • Standardizing transaction recording and exception handling.
  • Assigning improvement owners and measurable targets.
  • Monitoring results and sustaining operational improvements.
  Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan  
  • Review a warehouse scenario containing layout, transaction, inventory, and performance information.
  • Identify weaknesses across receiving, put-away, storage, picking, and dispatch.
  • Recommend appropriate FIFO or FEFO controls.
  • Develop a cycle counting and inventory reconciliation plan.
  • Propose controls to reduce shrinkage and improve traceability.
  • Select relevant KPIs and define their calculation and reporting requirements.
  • Produce a prioritized improvement plan with responsibilities and expected outcomes.
 

This course develops the foundational knowledge and practical skills needed to configure, administer, and maintain the ServiceNow platform. Participants explore platform navigation, instance configuration, application collaboration, self-service, automation, database management, security, and configuration migration. The proposed five-day duration accommodates foundational learning, configuration activities, and an integrated capstone. Duration 5 Days – 35 hrs.   Objectives...

Duration: 5 days – 35 hrs   Overview The “SOC Network and Threat Detection and Analysis” training course is designed to equip Security Operations Center (SOC) analysts and IT security professionals with the skills and knowledge required to detect, analyze, and respond to network threats effectively. This comprehensive course covers essential topics such as threat...

Duration 1 day – 7 hrs   Overview   This 1-day training builds upon basic warehouse operations knowledge and introduces key logistics concepts involved in the movement and coordination of goods—especially wet and dry food items—within and outside the warehouse. Participants will explore transport logistics, inbound and outbound coordination, documentation practices, and cold chain considerations,...

Duration 2 days – 14 hrs   Overview   This hands-on course provides an introduction to Splunk, a powerful platform for searching, monitoring, and analyzing machine-generated data. The training focuses on how developers and QA professionals can leverage Splunk to gain insights from logs and metrics, improve application observability, detect anomalies, and support test validation....

Duration 3 days – 21 hrs   Overview.   This course is designed for fresh graduates aspiring to build a career in Data Science. It introduces the fundamentals of data science, focusing on data analysis, visualization, and basic machine learning concepts using Python. The course provides hands-on practice with real-world datasets, equipping participants with the...

// BG EARTH WHEN NOT PLAYING

We use cookies on our website to personalize your experience by storing your preferences and recognizing repeat visits. By clicking “Accept”, you agree to the use of all cookies. You can also select “Cookie Settings” to adjust your preferences and provide more specific consent. Cookie Policy