Name: Prompt Engineering Software Development (GitHub Copilot)
Availability: InStock

Prompt Engineering Software Development (GitHub Copilot)

An advanced, practice-driven course for developers already using AI coding tools. The focus is working faster and safer with GitHub Copilot by mastering prompt patterns, context strategies, and integration with real repositories, tests, and CI.

What will you learn?

You will refine how you scope engineering tasks, design prompts, and steer Copilot to produce reliable code changes. You will connect prompts to your repos and workflows, apply guardrails with tests and policies, and adopt lightweight evaluation so quality improves over time.

After this training you will be confident in:

• Framing engineering tasks for Copilot and choosing effective prompting strategies

• Grounding generations with repository context, symbols, and documentation

• Driving code edits, explanations, and diffs across multi-file projects

• Tightening feedback loops with tests and acceptance criteria

• Integrating prompts into Git, code review, and CI

• Managing privacy, security, and IP considerations with clear auditability

Requirements:

• Strong command of Git and code review practices

• Familiarity with a CI system and package managers in at least one language

• Access to a non-sensitive repository suitable for training

• Copilot enabled in your IDE and GitHub account

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

Module 1: Copilot in the SDLC

Where Copilot helps in planning, coding, testing, and revie
Task decomposition, constraints, and acceptance criteria as prompt inputs
Reading outputs critically and deciding when to iterate or restart

Module 2: Preparing high quality context

Supplying the right files, symbols, configs, and logs without overload
Summarizing large code areas and capturing invariants to protect
Linking design docs and API references to raise answer quality

Module 3: Code generation and editing patterns

Requesting focused diffs, patch-style outputs, and commit messages
Stepwise prompts for adding a feature while preserving contracts
Refactoring prompts that keep public APIs stable

Module 4: Tests as guardrails and debugging

Writing or tightening tests before edits to bound behavior
Using failing tests and logs to steer the next iteration
Producing reviewer-friendly explanations and change notes

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Module 5: Prompt templates and reuse

Reusable scaffolds for bug fixes, feature stubs, and migrations
Embedding style, lint, and formatting rules as constraints
Team conventions for naming, structure, and checklists

Module 6: Git and CI integration

Branching approaches that complement AI-assisted changes
Pull request descriptions, risk callouts, and review checklists
Connecting prompts to automated tests and quality gates

Module 7: Retrieval and external tools

When to attach docs, tickets, and changelogs for grounding
Selecting utility tools the model should rely on, such as linters and test runners
Handling long-running tasks and chunked objectives

Module 8: Security, privacy, IP, and evaluation