Summary
Implementing mechanisms to correct and optimize your GenAI application can have many forms and can be implemented before, during, and after answers are generated. For optimal performance, you’ll want to train your GenAI model with high-quality data, supplement existing models with your specific use case data, and have thorough evaluation datasets and record the model’s performance to establish a baseline of accuracy.
Once you have that baseline, however, you can immediately begin improving upon it with the techniques discussed in this chapter. Among these techniques is one- or few-shot prompting. It involves providing the GenAI model with a single example or prompt to guide its response, enabling the model to generate relevant and contextually appropriate outputs with minimal training data. You can also try retrieving and reranking relevant documents or data points based on the user’s query, and then reordering these results to prioritize the most relevant...
