DocETL
product on 1 show · 8 statements across 1 episodes · said 13 times in 2 episodes since 2024
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Shreya Shankar: Practical DocETL users never have pre-annotated ground truth datasets
“So we're redoing our evaluation to be on data sets where we actually have ground truth from human annotators, but that's just not a practical setting. Like, nobody's coming to doc ETL with the ground truth.”
Shreya Shankar: DocETL builds semantic unstructured layers, not point-lookup RAG systems
“This is very different from traditional rag or Q&A or document processing for a chatbot. Like, the kinds of queries that people are, people want to use .etl for can be expressed as etl style sweep and harvest, kind of, I want to look at my entire dataset. I wo…”
Shreya Shankar: GPT-4o Mini reduces DocETL optimization cost by 90%
“The reason it was a hundred dollars, if I ran the optimizer with GPT-Foro mini as the LLMs, it would be 10 dollars. But we use GPT four. Oh, just because I think we did this at a time where many hadn't come out yet.”
Eugene Yan: DocETL pipeline optimizer costs roughly $100 and 30 minutes
“Running the optimizer, right, they used the optimizer. I don't know how many plans the optimizer generated, but it cost approximately a hundred dollars and less than half an hour. Just go get lunch and you come back and you get your optimized pipeline. And the…”
Eugene Yan: LLM pipeline validation still requires seed human-labeled data
“I'm of a slightly different take. I feel like we do need some set of seed human labeled data.”
Shreya Shankar: Chunking efficacy is task-specific, requiring automated pipeline optimization
“Sometimes it's beneficial to chunk and sometimes you should not chunk. And we have observed this in a number of workloads and the insight that we've gained is that we will never know what it's all task specific and data specific. And we are so further convince…”