Product & Engineering¶
This section teaches the concepts behind how software gets planned, built, and shipped. You don't need a technical background — these pages are written for business leaders, operators, and anyone who works alongside engineering teams or uses AI coding tools.
What Is Product Management?¶
Product management is the discipline of deciding what to build and why. A product manager (PM) identifies problems worth solving, defines what a solution looks like, prioritizes what to build first, and ensures the team stays aligned on the goal. The PM doesn't write the code — they write the requirements, make the trade-offs, and own the outcome.
What Is Software Engineering Process?¶
Software engineering process is the discipline of how to build it well. It covers the lifecycle of turning requirements into working software — planning the work, breaking it into manageable pieces, building and testing it, shipping it to users, and learning from the results. Good process prevents chaos. It gives teams a shared language, predictable cadences, and clear definitions of "done."
Why This Matters for You¶
Whether you're a business leader, a consultant, or someone learning to build with AI, understanding these concepts pays off in practical ways:
- Collaborate with engineering teams. Speak the same language. Understand what a sprint is, why a PRD matters, and how issues flow through a project board.
- Evaluate AI coding output. AI coding agents follow these same processes — they read requirements, work from acceptance criteria, create issues, and submit pull requests. Knowing the process helps you judge whether the output is good.
- Manage AI-driven development. If you're directing an AI agent to build something, you're acting as the product manager. The quality of your requirements directly determines the quality of what gets built.
What's in This Section¶
| Page | What You'll Learn |
|---|---|
| Software Development Lifecycle | How software gets built — the plan-build-test-ship cycle, Agile vs. Waterfall, sprints, and key roles |
| Product Requirements | PRDs — the document that aligns a team on what to build before they start |
| User Stories & Acceptance Criteria | The building blocks of requirements — how to describe what users need and define "done" |
| Roadmaps & Prioritization | Deciding what to build first — frameworks for making trade-offs |
| Stakeholder Management | The people side — communication, buy-in, and managing expectations |
| Project Tracking with GitHub | Issues, epics, projects, and pull requests — the tools teams use to organize work |
The AI Connection¶
Modern AI coding agents don't just write code — they follow the same workflows that human engineering teams use. An AI agent:
- Reads a PRD to understand what to build
- Works from acceptance criteria to know when it's done
- Creates issues to track individual tasks
- Opens pull requests that reference the original requirement
- Responds to review feedback and iterates
Understanding these concepts makes you a better operator of AI tools. When you write a clear requirement with testable acceptance criteria, the AI produces better output — because you've given it the same clarity that a well-run engineering team depends on.