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jlopes2

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Show HN: Phoenix OSS – Applying LLM Spans, Traces, and Evals for AI Insights

github.com
23 points·by jlopes2·hace 3 años·3 comments

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jlopes2
·hace 2 años·discuss
This feels a little to high level for me, the leap from what LLM does today to provable theorem set is rough.

community is having large debates on whether an LLM can reason outside of its training.This feels ignored in here.
jlopes2
·hace 2 años·discuss
Let’s see the code. A bit skeptical, this hasnt over complicated something architecturally. Need more clear drawings of architecture. What prompts exist, what tool calls are made, and what gets updated.
jlopes2
·hace 3 años·discuss
I just did a talk with Jerry from LlamaIndex earlier this week. https://www.youtube.com/watch?v=eLXivBehPGo

Included here is a bit of the old tried and true: NDCG/MRR/Precision @k - what you really want for measuring your information retrieval systems.

But we also talk through a bit of the "new", how to use Evals to generate the building blocks for those metrics above. You will want both hand labels and the automated Evals in the end to evaluate your system.