Copilot, AI coding certified instructor to record training videos for GH300, AZ2007 certifications

We are looking for a GitHub COPILT coding instructor to record training videos. All content and lab material will be provided. The certified trainer will be required to record training videos, where all the training content, and the content will be provided any errors in the lab content and lessons will have to be fixed during the course of recording mostly it is expected that the content is fine as provided please review the topics below and only expert copilot trainers are required to reply to this job. ================================ AI-ASSISTED APPLICATION DEVELOPMENT WITH GITHUB COPILOT Intermediate | 20 Lessons | 20 Hands-On Labs | 1 Capstone ================================================================================ ABOUT THIS COURSE -------------------------------------------------------------------------------- Developers learn to use AI assistance across the whole software development lifecycle rather than as an autocomplete feature. The course teaches the judgement that makes generated code safe to accept: how to write a request that cannot be misread, how to review what comes back, how to verify a control you configured, and how to decide when to reject a suggestion that looks right. Every lesson has a matching lab, and the twenty labs build one application. Students do not work through disconnected exercises - each lab consumes what the previous ones produced, and the final lab assembles all of it into a capstone deliverable. The running application is TaskFlow: a work-item tracker written in C#, with a JavaScript dashboard and a Python reporting utility. It is specified, built, documented, tested, repaired, hardened, governed, automated, reviewed and extended entirely through AI-assisted development. CERTIFICATIONS TRACKED -------------------------------------------------------------------------------- This is a harmonized two-credential course. Both blueprints are covered in full; overlapping material is taught once, at the depth the more demanding credential requires. GH-300 GitHub Copilot Six domains: responsible use; features and capabilities; data and architecture; prompt engineering and context crafting; developer productivity; privacy, content exclusions and safeguards. AZ-2007 Accelerate App Development by Using GitHub Copilot (assessment resource code APL-2007) Five hands-on skill areas: explaining code; documenting code; developing features; developing unit tests; refactoring, debugging and improving code. Coverage: 95 teaching objectives across Lessons 1-19. Every sub-objective in both blueprints maps to a lesson. Lesson 20 closes with a domain coverage map. WHO IT IS FOR -------------------------------------------------------------------------------- Level: Intermediate. A single-course track, not part of a numbered sequence. Prerequisites: working knowledge of Git and GitHub - clone, branch, commit, pull request - and prior programming experience in at least one language. Primary environment: Visual Studio Code with GitHub Copilot. C# is the primary language, with JavaScript and Python introduced in Lesson 19. LESSON OUTLINE -------------------------------------------------------------------------------- MODULE 1 - FOUNDATIONS AND ARCHITECTURE 1 AI-Assisted Development, Copilot Products, and Editor Setup The product family and plans, editor setup and authentication, and what accepting, rejecting or partially taking a suggestion actually commits you to. 2 Inside Copilot - Data Flow, Prompt Building, and the Suggestion Lifecycle How a request becomes a suggestion: context gathering, prompt construction, filtering, and the limitations that follow from the architecture. 3 Responsible AI - Risks, Harms, Mitigation, and Output Validation Generative AI risks in a development setting, and the three review passes - correctness, security, provenance - applied to every piece of generated code. MODULE 2 - PROMPTING AND CONTEXT 4 Prompt Structure, Context, and Crafting Best Practices What makes a request answerable: stating the rule rather than a judgement, naming the shape you expect, and supplying the context the assistant cannot see. 5 Zero-Shot, Few-Shot, and Iterative Prompt Engineering Choosing a technique deliberately rather than by habit, and knowing when a worked example helps and when it constrains the answer to the wrong shape. 6 Copilot Chat, Chat Commands, and Copilot Spaces Working conversationally, using chat commands, and grounding a Space in your own requirements so answers stop being generic. MODULE 3 - BUILDING THE APPLICATION WITH COPILOT 7 Explaining Projects, Files, and Dependencies Reading unfamiliar code with assistance, at four scopes, and verifying one claim before trusting the rest of an explanation. 8 Generating Documentation and Code Comments Producing documentation that states error conditions and assumptions, and testing it the only way that works - by following it from a clean checkout. 9 Developing Features, Data Structures, and Regular Expressions Building a feature from a written requirement clause, including the patterns and sample data, and testing a generated expression against cases it must reject. 10 Generating Unit Tests, Test Data, and Edge Cases Judging generated tests rather than collecting them: naming tests after rules, strengthening weak assertions, and building negative data sets deliberately. 11 Refactoring, Debugging, and Implementing Code Improvements Diagnosing a failure before fixing it - three facts, competing explanations, one change per validation cycle - and simplifying without changing behaviour. 12 Security Hardening, Performance, and Legacy Modernization Finding weaknesses nothing reports, hardening a boundary for shape, range and meaning, and measuring an optimization before accepting it. MODULE 4 - GOVERNANCE AND SAFEGUARDS 13 Privacy, Content Exclusions, Policy, and Audit Configuring content exclusions and organization policy, verifying that each control actually took effect, and retrieving audit evidence through the API. MODULE 5 - COPILOT ACROSS THE DEVELOPER WORKFLOW 14 Developing from the Command Line with GitHub Copilot CLI Explaining an unfamiliar command before running it, turning a goal into a command, and generating a build script that fails correctly. 15 AI-Assisted Code Review, Pull Requests, and Review Standards Triaging review comments rather than obeying them, writing a summary that states intent, and declaring team standards that reviews then apply. MODULE 6 - AGENTIC DEVELOPMENT 16 Building Features with Agent Mode and Copilot Edits Assigning multi-file work from a written requirement, reviewing an iterative change set one change at a time, and validating before anything is committed. 17 Delegating Work to the Cloud Agent, Sessions, and Sub-Agents Delegating a task from an issue, reconstructing unwatched work from a session log, and requesting changes on the pull request it opens. 18 Connecting Tools to Copilot with the GitHub MCP Server What the Model Context Protocol is, configuring and confirming a connection, and diagnosing authentication, transport and permission failures by stage. MODULE 7 - MULTI-LANGUAGE DEVELOPMENT 19 Cross-Language Development with JavaScript and Python Applying the same practices in two more runtimes, finding which transfer unchanged, and integrating three components across one shared data boundary. MODULE 8 - CAPSTONE CLOSE-OUT 20 Capstone Completion and Course Close-Out One final requirement through the entire workflow, everything integrated and verified, and an assessment of the assistance evidenced from the lab record. WHAT STUDENTS FINISH WITH -------------------------------------------------------------------------------- A complete repository: the C# application with its test suite, a JavaScript dashboard, a Python reporting module, documentation, build automation, a review standards file, connected-tool configuration, content exclusions, the pull request history, sixteen lab record files, and a capstone report assessing where AI assistance helped, where it needed correction, and what was validated by hand.