A Field Guide to Rapidly Improving AI Products
March 26, 2025
With new tools and frameworks emerging weekly, it’s natural to focus on tangible things we can control – which vector database to use, which LLM provider to choose, which agent framework to adopt. But after helping 30+ companies build AI products, I’ve discovered the teams who succeed barely talk about tools at all. Instead, they obsess over measurement and iteration.In this post, I’ll show you exactly how these successful teams operate. You’ll learn:
- Why focusing on tools over process is killing your AI projects
- How error analysis consistently reveals the highest-ROI improvements
- 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
Source: A Field Guide to Rapidly Improving AI Products – Hamel’s Blog
Right now when it coms to building products with AI, we are at the ‘obsess about the tools’ phase, but as Hamel Hussain observes, this is not what successful teams are doing–here he shares what they are.
