Harness the power of multiple LLMs 🤝

When dealing with LLMs like GPT-4, PaLM2, etc., we can often get varying outputs. This begs the question - how can we easily assess what the actual output is given these varied responses?

This is where a chain-of-thought prompting comes into play. By passing the same prompt across different LLMs, we can use them to verify and enhance the accuracy of the final output. In the example, we show here we use "marjority-vote / quorum" amongst the responses to determine …

ai etc gpt gpt-4 harness llms machinelearning multiple opensource palm2 power prompt prompting responses thought tutorial