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Atmrix

About

a

a16z Cast

05/29/25

@ a16z

The goal is to integrate subjective measurements into the model release pipeline, ensuring continuous evaluation post-release.

Video

a

Beyond Leaderboards: LMArena’s Mission to Make AI Reliable

@ a16z

Beyond Leaderboards: LMArena’s Mission to Make AI Reliable

05/29/25

Related Takeaways

a

a16z Cast

05/29/25

@ a16z

The goal is to integrate subjective measurements into the model release pipeline, ensuring continuous evaluation post-release.

WA

Waseem Alshikh

03/27/25

@ LangChain

We have developed an internal evaluation system to measure our models, as relying on external benchmarks often leads to data contamination and unreliable results.

HC

Harrison Chase

05/28/25

@ LangChain

The evaluation process is continuous; it's not enough to just build a dataset and run it once. Ongoing evaluations are necessary to maintain quality in production systems.

MK

Mike Krieger

05/09/25

@ Sequoia Capital

There's a challenge in balancing the release of models for external use versus deploying them internally for greater utility.

SN

Satya Nadella

05/22/25

@ Rowan Cheung

The virtuous cycle of improvement in model capability is driven by the application of knowledge to fine-tune new samples based on market feedback.

VM

Varun Mohan

05/02/25

@ Y Combinator

The AI tools we build need to continuously deliver additional value as the baseline for foundation models improves.

O

OpenAI Cast

04/11/25

@ OpenAI

Post-launch, the team continuously monitored the run for improvements and correctness, balancing the need for data efficiency with the challenges of noisy signals.

a

a16z Cast

05/23/25

@ a16z

Understanding the distribution of AI model behavior over time is essential for diagnosing performance issues and ensuring consistent outputs.

a

a16z Cast

05/29/25

@ a16z

As AI evolves, we need to shift from static benchmarks to real-time evaluation systems that can adapt to user feedback.