Error Analysis
A methodology in AI/ML engineering for systematically examining model or system failure cases. It involves collecting failure samples, categorizing them by error type, estimating impact frequency, and prioritizing high-proportion bottlenecks to drive iterative model improvement.
Core Facts
Timeline (last 90 days)
Andrew Ng believes that the greatest dividing line in truly building good Agentic AI applications is whether one possesses the ability to drive a disciplined development process centered on evaluation (evals) and error analysis.
Error analysis is a methodology for systematically examining failure cases; the process involves collecting failure samples, categorizing them by error type, estimating frequency and impact, and prioritizing the bottlenecks with the highest proportion.
The key to error analysis is pinpointing exactly which stage and which type of input caused the failure, rather than blindly adjusting prompts.
All Facts (3)
The key to error analysis is pinpointing exactly which stage and which type of input caused the failure, rather than blindly adjusting prompts.
70%UnverifiedAndrew Ng believes that the greatest dividing line in truly building good Agentic AI applications is whether one possesses the ability to drive a disciplined development process centered on evaluation (evals) and error analysis.
50%UnverifiedError analysis is a methodology for systematically examining failure cases; the process involves collecting failure samples, categorizing them by error type, estimating frequency and impact, and prioritizing the bottlenecks with the highest proportion.
50%