I’m seeing a worrying pattern lately. Almost every product discussion now starts with, “So, how are we using AI for this?”

We’re all a bit drunk on the hype. We’re treating AI like magic dust we can just sprinkle on any problem.

Clients want a Youtube or Netflix level recommendation engine on day one. Devs, quite reasonably, are excited to put the shiniest new Vector DB on their resumes.

We’re starting backward. We’re trying to build the penthouse while the foundation is still a sketch on a napkin.

The reason this “penthouse-first” approach fails is that we’re ignoring the reality of what an AI actually is. We treat it like a magic box we can just plug in, but it’s not.

You can’t “set-it-and-forget-it.” You have to manage it.

  • The world changes, there is a new trend?? Your AI employee will be confused because it has never seen this. It starts to drift right from Day 2.
  • It makes a mistake? It doesn’t self-correct. You have to build a feedback loop to fix it.
  • You want it to be smarter? You have to re-train it, and that is a continuous operational cost (OpEx).

When you understand AI is something you must maintain and not something you own, your entire architecture changes.

My Take: 99% Boring, 1% AI

So if AI is this high-maintenance “penthouse,” what’s the “foundation”?

My personal philosophy is simple: Never use expensive AI to do a job a good old SQL query can do.

The value isn’t in a single “black box” AI. The value is in a hybrid system where boring rules and code do 99% of the heavy lifting. The AI is just the 1% of spice you add at the very end.

A Practical 3-Phase “Boring” Roadmap (My Small Advice)

If I’m building a “smart” matching system, I will not start with AI. I build it in phases, layering complexity only when necessary.

Let’s walk through this with one single example: building a “Smart Candidate Search” for a recruitment platform.

Phase 1: The Foundation of Correctness (SQL Rules)

This is the 99% of the work. The goal here is 100% Correctness, enforcing the non-negotiable rules of the business. An “AI-first” system might think a candidate in Ho Chi Minh City is a “great match” for a job in Hanoi, but your business rules say that’s unacceptable. This layer is the “bouncer” at the door.

  • In Practice: WHERE location = 'Hanoi' AND salary_request < 5000 AND years_experience >= 3.

This filters on facts, not suggestions. location and years_experience are binary facts. We must do this first to avoid wasting expensive AI compute on candidates who are an immediate “no.” This is your blazing fast, dirt cheap foundation.

Phase 2: The Layer of Relevance (Full-Text Search)

This is still the 99% of the work. Now that we have a list of correct candidates (e.g., in Hanoi, >3 years exp), we solve for Relevance. Our SQL filter was correct, but “dumb” about human language. A recruiter searching “programmer” won’t find “developer.”

  • In Practice: Use Elasticsearch or BM25 (built into Postgres) on the resume text so “java programmer” matches “java developer.”

This 20-year-old boring tech is built specifically to solve the synonym problem without the cost or “black box” nature of AI. Critically, it’s explainable—you know why that resume showed up.

Phase 3: The 1% Layer of “Nuance” (The AI)

Only now, after our 10 million candidates have been filtered down to 1,000 correct (Phase 1) and relevant (Phase 2) candidates, do we add the final 1%: Nuance. Full-Text Search is great with words, but not ideas. This is the 1% problem AI is actually good at.

  • In Practice: The AI’s job is to know that a recruiter searching for “Senior Java” is conceptually similar to candidates strong in “Spring Boot” or “Scala” (even if they didn’t type those words).

This is the key to performance: an AI ranking 10 million items is impossibly slow and expensive. An AI ranking 1,000 correct and relevant items is near-instantaneous and cost-effective.

The AI is not the filter; it’s the re-ranker. We contain the expensive, unpredictable AI. We let it play only within the 1,000 safe results our boring filters found. This gives you the correctness of SQL plus the nuance of AI, but your costs drop 99% and your speed goes up 1000x.

Final Thought

The hype will pass. The core of good engineering isn’t using the shiniest tool. It’s the wisdom of knowing how and when to solve a problem.

Your “AI Strategy” shouldn’t be about AI. It should be about the system.

Stop building the penthouse first. Build your boring, indestructible foundation.