@bruce.dambrosio I just checked in a new Sentiment Analysis sample for the core library that has the model always return its answer via a JSON object that includes sentiment analysis for the users message. Looking at the sample run below you can see that GPT-3.5 pretty much immediately fails to return JSON which AlphaWave gets it to fix. It’s then fine from that point on and this is running a temp of 0.9:
Here’s the core code that sets up the response validator and prompt:
You should be able to extend this example to perform other general purpose language tasks (translation, language detection, classification, etc.) and get rock solid output from even gpt-3.5-turbo.