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2min chapter
Data augmentation with LlamaIndex
Practical AI: Machine Learning, Data Science, LLM
The Future of Data Retrieval
Jerry: I think there's always going to be this trade off between how few constraints can we give it and how many like should we give it. He says if you kind of like constrain the outputs of these like automated decision makers or agents then you can potentially get more interpretable output maybe at the cost of a little bit of flexibility in terms of functionality. Jerry: How can we give you back results that are expressive but also perform well and are going to make mistakes like a time of time awesome yeah well I'm really happy that we had a chance to talk through all the great llama index things, so check out llamaIndex.com for details on what they're working on
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