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Atmrix

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Atmrix

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HC

Harrison Chase

05/28/25

@ LangChain

Using LLMs as judges for evaluating outputs is promising, but it requires careful prompt engineering to ensure accurate grading of responses.

Video

L

How to Solve the #1 Blocker for Getting AI Agents in Production | LangChain Interrupt

@ LangChain

How to Solve the #1 Blocker for Getting AI Agents in Production | LangChain Interrupt

05/28/25

Related Takeaways

HC

Harrison Chase

05/28/25

@ LangChain

There are three types of evaluators: deterministic code-based evaluations, LLM-as-judge techniques for more complex assessments, and human annotation for real-time feedback.

TB

Tom Blomfield

04/25/25

@ Y Combinator

Monitor the LLM's responses closely; if it keeps generating incorrect code, reassess the context you're providing to improve its output.

AK

Andrej Karpathy

06/19/25

@ Y Combinator

We need to adjust our infrastructure to effectively work with LLMs, as they are fallible systems that require careful integration.

A

Anthropic Cast

02/28/25

@ Anthropic

The grading system in the demo was designed to evaluate the detail and helpfulness of responses, but it faced challenges in accurately assessing harmfulness versus helpfulness.

L

LangChain Cast

05/30/25

@ LangChain

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.

IS

Ion Stoica

05/29/25

@ a16z

As AI systems become more prevalent in critical industries, the need for robust evaluation platforms like LMArena becomes even more important.

C

Carlos

05/24/25

@ LangChain

Achieving high accuracy in enterprise use cases is challenging, especially when integrating SQL with LLMs, which do not work well together.

A

Anthropic Cast

02/28/25

@ Anthropic

Using a more capable classifier after serving a response can help flag potentially harmful outputs and involve human reviewers for the most dangerous responses.

IS

Ion Stoica

05/29/25

@ a16z

The unique aspect of LMArena is its ability to evolve over time, adapting to the changing landscape of AI evaluation.