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LLM Testing Lib

Objectives

  • Allow for easy testing of LLM calls
  • Speed up tests and save money of LLM calls by storing them in source

Concepts Applied

  • Use GPT-4 to assert that its own response meets a set of expectations
  • Store responses in source to increase test speed and save money

Playground

Clone this repo, experiment with the values in index.ts, then run npm run main to see how accurately your prompt meets a set of expectations. Ensure you have an environment variable set up for OPENAI_API_KEY and have access to GPT-4 via OpenAI's API.

The arguments for checkAccuracy are:

  • messages -- the history of messages, aka the conversation thus far. The last one will be the one being sent by the user
  • expectations -- an array of rules that you expect the response to meet
  • numAttempts -- depending on how accurate you need to be. If you need 99% accuracy, you would do (at least) 100 attempts
  • logFailures -- optional, best used when troubleshooting

Upcoming Improvements

  • List of what failed -- see which expectations failed (or why it failed from OpenAI's perspective)
  • UI+API for anyone to fork and deploy (perhaps using Next + Vercel)
  • Prompt comparison -- be able to compare accuracy of 2+ prompts, given a set of expectations
  • Change LLM's/models -- be able to change completion model and assertion model