A Field Guide to Rapidly Improving AI Products
hamel.dev- Category
- Other
- Type
- ARTICLE
- Builder
- @HamelHusain
- Added
- Jul 21, 2026
About
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 chang
What it can do
Teach error analysis methodology to identify high-ROI AI improvements
AI product outputs and failure cases → Prioritized list of the highest-impact improvements to make
Guide teams to build a simple data viewer for inspecting AI outputs
AI system traces and interaction data → A data viewing workflow for reviewing and understanding AI behavior
Explain how to empower domain experts to improve AI systems
Non-engineer domain expertise and AI evaluation needs → A process for involving domain experts in AI iteration
Demonstrate how to generate and use synthetic data effectively
Data scarcity problems and test scenarios → Synthetic data strategies for testing and improving AI
Provide methods to maintain trust in an AI evaluation system
Existing evaluation metrics and dashboards → Reliable, trustworthy evaluation practices
Show how to structure an AI roadmap around experiments rather than features
Team goals and product plans → An experiment-driven AI development roadmap
Illustrate measurement techniques for validating whether AI changes help or hurt
AI system changes and iterations → Measurable evidence of improvement or regression
Why it made the leaderboard
If your AI team obsesses over frameworks and vector DBs but can't tell whether changes actually help, this lays out a measurement-first workflow — error analysis, lightweight data viewers, synthetic data, and roadmaps that count experiments not features — drawn from 30+ real deployments.
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