Overview
Most AI projects fail not because models are inadequate, but because teams cannot define what “correct” means for their specific use case. Defining correctness is the foundation that determines everything else - from model choice to system architecture - yet humans often change quality definitions mid-project without updating their AI systems accordingly.
Key Takeaways
- Define correctness before building - If you can’t articulate what ‘correct’ means for your specific AI use case, you’re building on an unstable foundation that will cause project failure
- Acknowledge human inconsistency - Teams often change their quality standards mid-project without realizing it, then blame the AI system for being unreliable when the real issue is shifting human expectations
- Make quality definitions explicit and updateable - Write down your correctness criteria and build systems that can adapt when those definitions legitimately need to change, rather than hoping the AI will guess your evolving standards
- Architecture flows from quality definitions - Decisions about RAG systems, agents, model selection, and orchestration should be driven by your specific correctness requirements, not generic best practices
Topics Covered
- 0:00 - The Root Cause of AI Project Failures: Introduction to how most AI projects fail due to undefined correctness rather than model limitations
- 1:30 - The Measurement Problem: Explaining why you can’t improve what you can’t measure, and how undefined correctness creates shifting targets
- 3:00 - Downstream Architecture Decisions: How unclear quality definitions impact RAG systems, agents, orchestration, and model selection
- 4:30 - Human Inconsistency in Quality Standards: Discussing how humans change quality definitions mid-project without updating systems
- 6:00 - Building Quality-Centered AI Systems: The importance of making correctness central to AI architecture and allowing for predictable updates