Overview
Most AI projects fail not because models are inadequate, but because teams can’t clearly define what “correct” means for their specific use case. Defining correctness is fundamental - without it, you can’t measure performance or improve your system. All technical decisions about RAG, agents, and model selection become ineffective when built on undefined quality standards.
Key Takeaways
- Start with clear correctness definitions before choosing any AI technology - technical architecture decisions are meaningless without knowing what success looks like
- Document your quality standards explicitly - unwritten, socially-agreed definitions lead to scope creep and system blame when expectations shift
- Build measurement into your AI system architecture from day one - you cannot improve what you cannot quantify, making correctness metrics foundational
- Design for definition changes - quality standards will evolve, so build systems that can adapt to new correctness criteria in predictable ways
- Recognize that most AI failures are process failures, not model failures - the problem is usually unclear requirements, not insufficient model capability
Topics Covered
- 0:00 - The Root Cause of AI Project Failures: Most AI projects fail because teams can’t answer what ‘correct’ means, not because models are inadequate
- 0:15 - The Measurement Problem: Without defining correctness, you can’t measure it, and without measurement, improvement is impossible
- 0:30 - Downstream Technical Decisions: RAG systems, agents, orchestration, and model choices become ineffective when built on undefined targets
- 0:45 - Human Inconsistency in Definitions: Humans often change quality definitions midstream without documentation, then blame systems for being unreliable
- 1:00 - Architecture-First Approach to Quality: Building systems with correctness and quality definitions at the architectural core, allowing for predictable updates