AI Engineer Course / The Framework Onboarding Edition

The AI
Engineer
Playbook

From AI-Assisted → AI-Native

8 engineering workflows ranked by time drain × automation value, mapped to a 3-level skill progression — from pasting snippets into a chat box to running your own agentic dev pipeline.

01 The Top 5 Time Killers
1
Onboarding to an unfamiliar codebase
The universal cold-start tax — days of grep-and-read before the first commit
2
Debugging failures & stack traces
Hours lost to repro and root-cause on flaky, unfamiliar errors
3
Writing & maintaining tests
The first thing skipped when time is short — coverage quietly rots
4
Code review
PRs pile up; context lives in one head and reviews stall
5
Boilerplate & scaffolding new code
The same setup, hand-written again on every new module
02 The 3-Level Progression
01 Basic
AI-Assisted Engineer
Core shift Engineer brings context to AI (copy-paste snippets)
Key unlock Prompt craftsmanship
Modules
Snippet generation Code explanation Debugging Q&A Boilerplate drafting Docs & syntax lookup
Tools
Claude Chat ChatGPT Web search

Who's here: engineers using AI as a smarter rubber duck — snippets and explanations, mostly copy-paste.

+ Repo Context
▼
02 Intermediate
AI-Connected Engineer
Core shift AI works inside your repo with full project context
Key unlock Context-aware editor & MCP tooling
Modules
In-editor code generation Context-aware debugging Test generation AI code review Repo-aware onboarding MCP integrations
Tools
GitHub Copilot Cursor Claude Code GitHub MCP Sentry MCP Postgres MCP

Who's here: engineers wiring AI into their actual editor and repo — not yet end to end across tests and reviews.

+ Autonomy
▼
03 Expert
AI-Native Engineer
Core shift Engineer builds agent pipelines that plan, edit, test & review
Key unlock AI as infrastructure, not autocomplete
Modules
Multi-file agent changes Self-verifying fixes Coverage-maintaining agents Automated review pipeline Persistent codebase context CI-integrated agents Multi-agent orchestration
Tools
Claude Code Custom skills Subagents CI agent pipelines

Who's here: engineers building their own agentic dev system across a persistent codebase context.

“
You don't need a better AI. You need to give your AI better context. Each level wires in more of your repo, tests, and tooling — which makes AI reasoning more useful, and unlocks the “pair that already knows the codebase” every engineer wants.
The thesis of the entire course
03 What Makes It Personal
01
Their actual repo & architecture conventions How this team really structures and names things.
02
Their test framework & patterns Jest? Pytest? Table-driven? However they really test.
03
Their code-review standards The checklist and conventions a PR has to pass.
04
Their stack & tooling map Which services, which CI, which observability tools.
05
Their domain & service glossary The language the system and the team actually speak.

No two codebases work the same way.
The AI has to match YOUR system.

04 The MCP Integration Stack
GitHub MCP
Repos & PRs
→ Code & review intelligence
Sentry MCP
Errors
→ Stack-trace analysis
Postgres MCP
Database
→ Schema & query context
Filesystem MCP
Codebase
→ Repo-wide context
Linear / Jira MCP
Tickets
→ Issue intelligence
Playwright MCP
Testing
→ End-to-end automation