From Vibes to Metrics: A Tech Exec’s Guide to Better AI Performance

Hello folks! 👋 Welcome to another issue of Tactical Thursday!

Today, we’re diving into a critical but often overlooked topic- how to systematically improve your Gen AI applications.

Too often, product teams focus on rolling out shiny new features while neglecting to optimize what’s already there. But without improving existing capabilities, your AI app may never reach its full potential.

Let’s break down a clear approach to tuning your AI for real-world performance.

🥱 The Lazy Vibes Check

A common but flawed method for assessing AI performance is the "vibe check".

Here’s how it typically plays out: An engineer builds an AI app, runs a few sample inputs, and checks if the outputs "feel right". If things seem okay, the app gets shipped.

While vibes checks can highlight obvious failures, they don’t:

• ✅ Cover a wide variety of use cases
• ✅ Provide a structured way to measure progress over time

It’s like going to the gym, randomly picking machines, doing a few reps, and hoping for gains. 💪 Any seasoned gym bro will tell you- that’s not how progress is made.

So, what’s the better approach?

🎯 Enter: The Eval Set

As a tech leader, if you want to systematically improve your AI application, you need an evaluation set (eval set).

Think of it as a consistent benchmark- a set of test inputs where you can compare actual vs. expected outputs.

Sounds like a test from school, right? 📚 But here’s the twist- you’re not supposed to score 100%. Because if you do, one (or more) of these problems exist:

1. Your eval set is too easy.
2. It doesn’t cover enough real-world scenarios.
3. You’re not adding failure cases as new issues arise.

Now that you know why eval sets matter, let’s get tactical.

📈 A Systematic Approach to Improving Your AI App

Follow these steps to continuously refine your AI application:

1. Gather diverse inputs. Use real user queries or generate synthetic data with LLMs.
2. Set up unit tests for basic functionality (e.g., handling empty inputs, invalid data formats).
3. Log everything! Track inputs, outputs, and LLM calls in a database.
4. Review failure cases regularly. Look at logs and identify where the app struggles.
5. Expand your eval set with those failure cases to ensure future improvements are measured.
6. Optimize continuously:
   • First, tweak prompts via prompt engineering. This is the fastest way to iterate. Keep refining until gains plateau.
   • Then, consider fine-tuning. Train on failure cases to systematically improve your model over time.

📌 Vibes Checks Won't Cut It

Improving AI applications isn’t complicated, but it’s also not glamorous- which is why many teams overlook it.

But remember: Your goal isn’t to do the "sexy" thing. It’s to build an AI system that actually works.

Try out this structured process and let me know your thoughts in the comments! ⬇️