Git with AI-Assisted Development training course

Highlights

Master Git & GitHub for Modern Development
Set Up Your GitHub Development Environment
Learn the Essentials of Git & GitHub
Manage Code Across Local & Remote Repositories
Build Confidence with Branching & Merging
Adopt Effective Git Workflows
Collaborate Through Pull Requests & Code Reviews
Plan & Manage Work with GitHub Projects
Accelerate Development with AI
Automate Testing & Delivery with GitHub Actions
Build Security into the Development Lifecycle
Secure Dependencies & the Software Supply Chain
Test & Secure Running Applications

Course Details

Day 1 — GitHub Foundations & AI-Assisted Development

Module 1: Introduction to Git & GitHub

• Version control concepts

• Git and GitHub

• GitHub vs GitLab and Bitbucket

• Repositories and collaboration

• GitHub development lifecycle

• Introduction to AI-assisted development

Module 2: Installation & Configuration

• Installing Git

• Git configuration

• GitHub authentication

• SSH and HTTPS

• GitHub CLI and GUI tools

• AI-assisted development environment

Module 3: Git & GitHub Terminology

• Repository and working tree

• Staging and commits

• Branches

• Clone, Push, Pull and Fetch

• Merge and Rebase

• Forks and Pull Requests

• Tags and Releases

Module 4: Local & Remote Repository Actions

• Creating and managing repositories

• Core Git commands and workflow

• Tracking, committing and reviewing changes

• Cloning and synchronising remote repositories

• Pushing, pulling and fetching changes

AI Assistance

• Explain Git commands

• Diagnose Git errors

• Analyse commit history

• Suggest troubleshooting approaches

Module 5: Branching & Merging

• Feature branches

• Branch management

• Merging

• Fast-forward and three-way merges

• Rebasing

• Merge conflicts

• Conflict resolution

AI-Assisted Exercise

• Explain conflicts

• Suggest resolution approaches

• Compare alternatives

• Generate validation steps

Module 6: Git Workflows

• Feature Branch workflow

• GitHub Flow

• Gitflow

• Forking workflow

• Trunk-based development

• Choosing an appropriate workflow

AI Exercise

Use AI to compare workflows and recommend an approach for different development

scenarios.

Module 7: Pull Requests & Code Review

• Creating Pull Requests

• PR descriptions

• Code reviews

• Review comments

• Suggested changes

• Approvals and merging

• Branch protection

AI-Assisted Code Review

• Code quality

• Bugs

• Security issues

• Missing tests

• Maintainability

• Refactoring opportunities

Module 8: Issues & GitHub Projects

• Issues

• Bugs and features

• Acceptance criteria

• Labels and milestones

• GitHub Projects

• Kanban workflows

• Linking Issues and Pull Requests

AI Assistance

• Generate Issue descriptions

• Break requirements into tasks

• Create acceptance criteria

• Generate test scenarios

Day 1 Practical Challenge

AI-Assisted Collaborative Development

Day 2 — AI, CI/CD & DevSecOps

Module 9: Advanced AI-Assisted Development

• AI prompting and code generation

• Code explanation and refactoring

• Debugging and testing

• AI-assisted troubleshooting

• Documentation

• Validating AI-generated code

• Responsible AI use

Practical Exercise

Learners use AI to:

• Analyse requirements and existing code

• Design and generate solutions

• Refactor and improve code

• Generate and review tests

• Diagnose problems and security issues

• Create technical documentation

• Validate AI-generated output

Module 10: GitHub Actions & CI/CD

• CI/CD concepts

• GitHub Actions

• Workflows and YAML

• Events and triggers

• Jobs and steps

• Runners

• Automated testing

• Workflow logs

AI-Assisted Pipeline Development

Learners use AI to:

• Create a workflow

• Explain YAML

• Troubleshoot failures

• Add tests

• Improve workflows

Module 11: DevSecOps & SAST

• DevSecOps principles

• Shift-left security

• Secure coding

• SAST

• Security findings

• Vulnerability remediation

• Security gates

AI Security Assistance

Learners use AI to:

• Explain vulnerabilities

• Identify root causes

• Suggest remediation

• Generate security tests

• Review proposed fixes

Module 12: SCA & Dependency Security

• Software Composition Analysis

• Dependency vulnerabilities

• Dependabot

• Dependency review

• Supply-chain security

• Dependency remediation

AI Assistance

• Explain dependency vulnerabilities

• Assess potential updates

• Identify breaking-change risks

• Generate test plans

Module 13: DAST & Application Security

• Dynamic Application Security Testing

• SAST vs SCA vs DAST

• Testing running applications

• Security findings

• DAST in CI/CD

AI-Assisted Security Investigation

Learners use AI to analyse DAST findings and develop a remediation and testing plan.

Who should attend

This course is aimed at developers, testers, DevOps engineers, support engineers and system administrators who want practical experience using GitHub and AI throughout the modern software development lifecycle.

The course particularly emphasises practical AI assistance, showing learners how AI can support development, testing, troubleshooting, code review, security analysis and documentation while retaining human responsibility for the final solution.

Prerequisites:

Basic command-line knowledge is useful but not essential. No previous GitHub Actions, AI or DevSecOps experience is required.

Feedback

4.8 out of 5 average

"I really liked how the lab work was integrated throughout the course"

JS, Software Developer, May 22

“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

Certification

Every delegate will be entitled to a certificate of achievement on completion of the course.

If you are missing your certificate - please use the link below to apply - you can also use this link to sign up for the JBI Training newsletter to receive technology tips directly from our instructors - Analytics, AI, ML, DevOps, Web, Backend and Security.

More about this course

This practical, hands-on course teaches modern software development with Git, GitHub, AI, CI/CD, and DevSecOps.

Learners will build confidence in managing code, collaborating through GitHub, working with branches and pull requests, automating workflows with GitHub Actions, and applying security throughout the development lifecycle.

The course also introduces AI-assisted development, showing learners how to use AI responsibly to write, review, test, debug, document, and improve code.

Through practical exercises and real-world development scenarios, learners will gain the skills to work more efficiently, collaborate effectively, and build secure, automated software delivery workflows.

FAQs

JBI Training offers three courses in this group. Git for Beginners is a two-day programme for those new to version control. GitLab CI — Pipelines and CI/CD is a one-day course covering GitLab's continuous integration and delivery pipeline features. Agentic Coding with GitHub Copilot is a three-day course covering how to work professionally with GitHub Copilot's agentic coding capabilities. All courses are available as scheduled classroom sessions in London, as live online instructor-led training, or as customised onsite programmes for development teams.

The Git for Beginners course is a two-day practical programme for developers, testers, and technical professionals who are new to Git or version control. It covers the core concepts of distributed version control, initialising and cloning repositories, staging and committing changes, working with branches and merging, resolving merge conflicts, working with remote repositories on platforms such as GitHub, GitLab, and Azure DevOps, using pull requests for collaborative code review, and applying standard Git workflows used in Agile and DevOps teams. No prior Git experience is required and the course uses hands-on exercises throughout to build confidence with everyday Git operations.

Git is a distributed version control system — a tool that runs locally and tracks changes to files and source code. GitHub, GitLab, and Azure DevOps are cloud-based platforms that host Git repositories and add collaboration, project management, and automation features on top. GitHub is the most widely used platform globally, particularly in open-source and technology company environments. GitLab is popular in organisations that want tighter integration between source control, CI/CD pipelines, and DevSecOps tooling — and is available as both a cloud service and a self-hosted installation. Azure DevOps is Microsoft's platform and integrates closely with the broader Azure and Microsoft ecosystem including Azure Pipelines, Boards, and Artifacts. JBI's Git training covers Git itself alongside practical use of these platforms.

The GitLab CI Pipelines and CI/CD course is a one-day programme covering GitLab's built-in continuous integration and delivery capabilities. It covers the structure and syntax of GitLab CI/CD pipeline configuration using the .gitlab-ci.yml file, defining jobs and stages, using runners, working with variables and secrets, building and testing code automatically on each commit, deploying to different environments, using GitLab's merge request pipelines, and applying best practices for reliable and maintainable CI/CD pipelines. It is suited to developers, DevOps engineers, and platform engineers who are using or adopting GitLab as their CI/CD platform.

Git branching strategies define how a development team uses branches to organise their work, manage releases, and collaborate without interfering with each other's changes. Common strategies include Git Flow (using long-lived feature, develop, release, and main branches), GitHub Flow (a simpler model based on short-lived feature branches and frequent merges to main), and trunk-based development (where all developers commit to a single main branch frequently, relying on feature flags to manage unfinished work). The right strategy depends on the team's size, release cadence, and deployment model. JBI's Git training covers branching strategies and helps teams understand the trade-offs between approaches so they can adopt a workflow that suits their development process.

Agentic Coding with GitHub Copilot is a three-day course covering how to work professionally with GitHub Copilot's agent mode — where Copilot can plan, write, execute, and test code across multiple files and steps to complete a development task, rather than simply completing a single line or function. The course covers how to specify tasks clearly for agentic workflows, how to evaluate and verify AI-generated code using test-driven development, how to steer agent behaviour when it goes off course, and how to integrate agentic coding tools into a professional development workflow responsibly. It is suited to software developers who are already using or planning to adopt GitHub Copilot and want to use its agentic capabilities effectively and safely.

Yes. All Git courses at JBI can be delivered as customised onsite or online programmes for corporate development teams. Content can be tailored to the team's preferred Git hosting platform — GitHub, GitLab, or Azure DevOps — their existing branching strategy and repository structure, and specific workflow challenges the team wants to address, such as improving pull request discipline, reducing merge conflicts, or adopting a consistent branching model across multiple teams. Training can also use the team's own repositories as the basis for practical exercises, making the learning immediately applicable to their real working environment. JBI has delivered Git and DevOps training for development teams at organisations including the BBC, NHS, RBS, Sky, EDF, and Cisco.

Yes. Git itself is stable and mature, but the platforms built around it — particularly GitHub, GitLab, and Azure DevOps — evolve continuously with new features for CI/CD, security scanning, code review, and AI-assisted development. GitHub Copilot in particular is updated frequently with new capabilities including improved agentic features, workspace integrations, and expanded language support. JBI's training content is continuously reviewed to reflect the latest versions of these platforms and tools, ensuring delegates learn skills and workflows that are current and applicable to the environments they work in today.

This course places AI assistance at the centre of the GitHub development workflow. Learners use AI to understand requirements, explore code, develop features, generate tests, troubleshoot problems, review Pull Requests, analyse security findings and produce documentation.

Public Courses

Customised Courses

Highlights

Course Details

Who should attend

Feedback

4.8 out of 5 average

Certification

More about this course

FAQs

Highlights

Course Details

Who should attend

Feedback

4.8 out of 5 average

Certification

More about this course

FAQs

What Git and version control training courses does JBI Training offer?

What does the Git for Beginners course cover?

What is the difference between Git, GitHub, GitLab, and Azure DevOps?

What does the GitLab CI — Pipelines and CI/CD course cover?

What branching strategies are covered in Git training and why do they matter?

What is Agentic Coding with GitHub Copilot and who should attend?

Can Git training be customised for a corporate development team?

Are Git and GitHub Copilot courses kept up to date with the latest platform releases?

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