Per-Session Breakdown

10 sessions evaluated · grouped by sessions.title · 6 consumer + 4 enterprise

consumer

98 answers · 60 passed

61.2%

Mean correctness 0.83 · Mean opening 0.49

enterprise

44 answers · 31 passed

70.5%

Mean correctness 0.86 · Mean opening 0.55

All Sessions

most recent first · click a row to drill in

Session Title	Tier	Role	N	Pass	Correctness	Completeness	Context	Context	Opening	Voice	Faithfulness
Stripe — Senior ML Engineer	consumer	ml_engineer	12	75%	0.86	0.79	0.89	0.83	0.58	0.73	0.94
Google — Staff ML Engineer	enterprise	ml_engineer	19	74%	0.87	0.81	0.89	0.85	0.56	0.72	0.94
Meta — MLOps Lead	enterprise	mlops_engineer	14	64%	0.83	0.74	0.86	0.81	0.50	0.69	0.92
Anthropic — LLM Inference Eng	consumer	llm_genai_engineer	16	50%	0.78	0.62	0.80	0.74	0.38	0.60	0.88
Databricks — Senior Data Eng	enterprise	data_engineer	11	82%	0.88	0.83	0.89	0.86	0.62	0.76	0.94
Snowflake — Data Platform Eng	consumer	data_engineer	9	78%	0.86	0.80	0.87	0.83	0.58	0.72	0.93
Netflix — Backend SWE	consumer	backend_engineer	13	62%	0.83	0.71	0.84	0.81	0.46	0.65	0.92
Airbnb — Frontend SWE	consumer	frontend_engineer	8	75%	0.82	0.78	0.86	0.82	0.54	0.71	0.91
Cloudflare — SRE	enterprise	sre	10	80%	0.86	0.79	0.87	0.84	0.57	0.74	0.93
Stripe — ML Infra round 2	consumer	mlops_engineer	11	55%	0.81	0.71	0.83	0.78	0.43	0.65	0.90