Overview
Most AI projects fail not because models are inadequate, but because teams cannot define what “correct” means for their specific use case. Correctness is upstream of everything - without clear quality definitions, all technical decisions become elaborate solutions built on shifting, unmeasured targets. Organizations must prioritize defining and measuring quality before making architectural choices.
Key Takeaways
- Define correctness before building - If you can’t articulate what “good” looks like, you can’t measure or improve your AI system’s performance
- Humans frequently change quality definitions mid-project without documentation, then blame the AI for being unreliable - establish written quality standards that the entire team commits to
- All technical decisions (RAG systems, agents, model selection) become meaningless without a clear target - quality definitions should drive architecture choices, not the reverse
- Build systems that can adapt when quality definitions evolve - create predictable ways to update AI processes when your understanding of “good” changes
Topics Covered
- 0:00 - The Core Problem with AI Projects: Most AI failures stem from inability to define what “correct” means, not from inadequate models
- 0:30 - The Human Factor in Quality Drift: How teams unconsciously change quality definitions mid-project and blame systems for inconsistency
- 1:00 - Building Quality-Centered AI Architecture: Designing systems where correctness definitions can predictably influence AI processes and responses