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These principles are not new. But their application in the messy reality of production search was a powerful lesson.

I share the full story here:

https://www.sebastiansigl.com/blog/rebuilding-search-lessons-learned

❌ Common Pitfall: Equating technical excellence with product success.

✅ The Principle: A product mindset is the true compass.

The goal is not the most sophisticated system; it's the most effective system for the user.

❌ Common Pitfall: Rigid functional roles and hand-offs.

✅ The Principle: Blurring lines creates synergy.

Empower your team. Our progress exploded when data scientists could run A/B tests & engineers could explore data.

❌ Common Pitfall: Chasing offline metrics (nDCG, precision).

✅ The Principle: Business impact is the north star.

If an experiment doesn't move a core KPI (engagement, retention), it's not an improvement.

❌ Common Pitfall: Engineering for "correctness" from day one.

✅ The Principle: Velocity unlocks correctness.

A fast, end-to-end feedback loop (from user action to A/B test) is the only path to finding what "correct" actually is.

❌ Common Pitfall: Treating search as just an algo/infra problem.

✅ The Principle: It's a Data & Product problem first.

An architecture that learns fast from user signals beats one that just serves fast.