Situation
Two years ago, I was drowning in code reviews. My team of 12 engineers was shipping fast — too fast. Every pull request (PR) needed my sign-off, but I was spending 4–5 hours daily on reviews. Bottlenecks, late-night approvals, and frustrated devs. Sound familiar?
I’m a Tech Lead at a mid-size SaaS company. We use TypeScript across the stack. Reviews were manual: check for style, logic errors, performance issues, and architectural consistency. The problem? Consistency. I’d miss things, repeat comments, and waste time on trivial style nits.
I needed a system — not just tools, but a process that scaled with the team. That’s when I combined AI-powered code review with structured mentorship patterns. The result: review time dropped 40%, and my team’s code quality improved without me becoming the bottleneck.
Approach
My approach had three pillars:
- Automate the boring stuff — Use AI to catch type errors, style violations, and common anti-patterns before I even look at the code.
- Pattern-based mentorship — Instead of rewriting code, I created reusable TypeScript patterns and taught the team to apply them.
- Shift left on architecture — Catch design issues early via lightweight RFCs (Request for Comments) and ADRs (Architecture Decision Records).
I started small. No big bang rollout. Just one team, one sprint.
Tools
Here’s the stack I used:
| Tool | Purpose | Why I chose it |
|---|---|---|
| GitHub Actions + ESLint with TypeScript rules | Automated linting and type-checking | Free, fast, and team already used it |
| CodeRabbit (AI reviewer) | AI-driven PR comments on logic, edge cases, and patterns | Understands TypeScript deeply; integrates with GitHub |
| Custom TypeScript patterns library | A shared repo with documented patterns (e.g., discriminated unions, builder pattern) | Team can reference and reuse; reduces repetitive comments |
| Notion + ADR templates | Document architectural decisions | Lightweight; no overhead |
I didn’t invent new tools. I just wired them together with clear workflows.
Practice
Here’s the exact process I followed:
Step 1: Automate the obvious
I configured ESLint with strict TypeScript rules (@typescript-eslint/strict). Then I added a GitHub Action that runs on every PR. If lint or type-check fails, the PR can’t be merged. This eliminated 30% of my review comments instantly.
Step 2: AI as a first reviewer
I enabled CodeRabbit to comment on every PR. It catches:
- Missing edge cases (e.g., null checks, undefined handling)
- Inconsistent error handling
- Overly complex conditions
Example AI comment:
"Consider using a discriminated union here instead of multiple
ifstatements. This pattern improves readability and type safety."
I only review after AI has passed. My review now focuses on architecture, not formatting.
Step 3: Build a pattern library
I created a patterns/ folder in the repo with TypeScript examples:
// before: scattered if-else
function handleStatus(status: string) {
if (status === 'active') { /* ... */ }
else if (status === 'inactive') { /* ... */ }
}
// after: discriminated union
type Status = { kind: 'active'; data: ActiveData } | { kind: 'inactive'; reason: string };
function handleStatus(status: Status) {
switch (status.kind) {
case 'active': /* ... */ break;
case 'inactive': /* ... */ break;
}
}
I documented 15 patterns in 3 weeks. Each pattern had a clear problem, solution, and trade-offs.
Step 4: Mentorship through RFCs
Instead of reviewing code architecture post-factum, I introduced lightweight RFCs for any change affecting more than one module. The team writes a 1-page doc, I review it in 15 minutes, and they code. This cut rework by 50%.
Results
After 3 months:
| Metric | Before | After | Change |
|---|---|---|---|
| Average review time per PR | 45 min | 27 min | -40% |
| Number of review cycles per PR | 2.8 | 1.6 | -43% |
| Team satisfaction with reviews | 6/10 | 8.5/10 | +42% |
| Code defects in production | 12/quarter | 5/quarter | -58% |
My team now reviews each other’s code using the same patterns. I only step in for cross-module decisions.
Takeaway
You don’t need a massive budget or a full-time DevOps. You need:
- Automation to handle the mundane
- Patterns to encode your experience
- AI to scale your attention
The biggest lesson: AI doesn’t replace a Tech Lead’s judgment. It amplifies it. I still make the final calls on architecture and trade-offs. But now I spend my time on what matters — mentoring, strategy, and crisis management — not hunting for missing semicolons.
If you want to dive deeper into these practices — ADR, RFC, delegation, and performance reviews — the team at ASI Biont has built a comprehensive Tech Lead course covering exactly this. It’s practical, no fluff, and based on real-world scenarios. Check it out at asibiont.com.
Start small. Pick one pattern. Automate one check. You’ll be surprised how fast the gains compound.
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