A Practical Guide to Rapidly Improving AI Products

Most AI teams focus on the wrong things. Here’s a common scene from my consulting work:

AI Team: “Here’s our agent architecture—we’ve got RAG here, a router there, and we’re using this new framework for…”

Me: [Holding up my hand to pause the enthusiastic tech lead] “Can you show me how you’re measuring if any of this actually works?”

… Room goes quiet

This scene has played out dozens of times over the last two years. Teams invest weeks building complex AI systems but can’t tell me if their changes are helping or hurting.

This isn’t surprising. With new tools and frameworks emerging weekly, it’s natural to focus on tangible things we can control—which vector database to use, which large language model (LLM) provider to choose, which agent framework to adopt. But after helping 30+ companies build AI products, I’ve discovered that the teams who succeed barely talk about tools at all. Instead, they obsess over measurement and iteration.

In this report, I’ll show you exactly how these successful teams operate. You’ll learn:

• How error analysis consistently reveals the improvements with the highest Return on Investment (ROI)

• Why a simple data viewer is your most important AI investment

• How to empower domain experts (not just engineers) to improve your AI

• Why synthetic data is more effective than you think

• How to maintain trust in your evaluation system

• Why your AI roadmap should count experiments, not features ...