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Imagine you got the smartest person in the world, locked them in a room without internet, and asked them to answer a bunch of random trivia questions, with only a few seconds for each one. Now imagine the same test, but this time you give the person access to a smartphone with Google and a calculator. Which test would go better?
This seems to be the essential logic behind recent techniques for improving the accuracy of large language models. LLMs locked in a room tend to make things up; why not let them use Google and a calculator too?
In this post, I show how I composed a simple AI program that can answer multi-part questions about NBA statistics. It uses GPT-3 as a general-purpose LLM “agent”, and calls out to Statmuse, a specialized natural-language search engine for sports statistics. The interaction between the two is orchestrated by Langchain, a Python library that helps compose “chains” of LLM behavior.
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