Uploaded July 2026 | Updated September 2026, 3 weeks ago
Learn the eval mistakes that quietly turn a useful metric into noise. A bad eval is worse than no eval at all: it gives you false confidence while the real problem stays hidden.
Cramming accuracy, tone, completeness, and formatting into a single eval feels efficient, but it hides exactly which one is actually failing.
Watch this to learn:
• Why cramming multiple criteria into one eval hides what's actually wrong
• The difference between guardrail evals (ship-blockers) and North-star metrics, and why it changes how you alert
• How to split evals so they stay focused, sharp, and trustworthy at scale
Chapters:
00:00 How not to write evals
00:14 Mistake: cramming many criteria into one eval
01:16 Split into focused, single-purpose evals
01:29 Guardrail evals vs. North-star metrics
02:31 Keep evals sharp and trustworthy
03:10 Next: calibrating your judge
👉 Sign up for free: app.arize.com/auth/login?utm_source=youtube&utm_medium=organic_social&utm_campaign=arize_ax_getting_started
🔗 Learn more about Arize AX: arize.com
📓 Colab notebook for the series: colab.research.google.com/drive/1dViThD0kJjbqtDGE7-ciBYIRwV-HBN1T
📚 Docs: docs.arize.com
🔔 Subscribe for weekly content on LLMs, agents, and evaluation: youtube.com/@arizeai?sub_confirmation=1
#ArizeAX #LLMEvaluation #AgentObservability
Learn the eval mistakes that quietly turn a useful metric into noise. A bad eval is worse than no eval at all: it gives you false confidence while the real problem stays hidden.
Cramming accuracy, tone, completeness, and formatting into a single eval feels efficient, but it hides exactly which one is actually failing.
Watch this to learn:
• Why cramming multiple criteria into one eval hides what's actually wrong
• The difference between guardrail evals (ship-blockers) and North-star metrics, and why it changes how you alert
• How to split evals so they stay focused, sharp, and trustworthy at scale
Chapters:
00:00 How not to write evals
00:14 Mistake: cramming many criteria into one eval
01:16 Split into focused, single-purpose evals
01:29 Guardrail evals vs. North-star metrics
02:31 Keep evals sharp and trustworthy
03:10 Next: calibrating your judge
👉 Sign up for free: app.arize.com/auth/login?utm_source=youtube&utm_medium=organic_social&utm_campaign=arize_ax_getting_started
🔗 Learn more about Arize AX: arize.com
📓 Colab notebook for the series: colab.research.google.com/drive/1dViThD0kJjbqtDGE7-ciBYIRwV-HBN1T
📚 Docs: docs.arize.com
🔔 Subscribe for weekly content on LLMs, agents, and evaluation: youtube.com/@arizeai?sub_confirmation=1
#ArizeAX #LLMEvaluation #AgentObservability
