DATABRICKS-CERTIFIED-GENERATIVE-AI-ENGINEER-ASSOCIATE Web TestEngine demo

Directly modify the LLM's internal architecture to include preprocessing steps

It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts

Rather than preprocessing prompts, it's more effective to postprocess the LLM outputs to align the

outputs to desired outcomes

Write a MLflow PyFunc model that has a separate function to process the prompts