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Mark White Mark White

R and Python Together: Refactoring and Prompt Engineering A Previous Case Study, Using the Perplexity API

I wrote a post last year looking at how to employ tools in LangChain to have GPT-3.5 Turbo access information on the web, outside of its training data.

The purpose of the present post is to revisit this post, improving the poor performance I saw there through refactoring and prompt engineering.

Background

The motivating example is again using large language models (LLMs) to help me calculate features for my Oscar model. Specifically: How many films did the director of a Best Picture nominee direct before the nominated film?

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