As I consume AI NLP responses I very often accept without much resistance, second guessing, or contemplation for or against the result presented to me. I simply read it and if I agree, I take it as true and continue forward with my activity. If I don’t agree I tend to respond with my doubt […]
Read More →
There are numerous ways to optimize model performance. Retrieval Augmented Generation (RAG) and Prompt Engineering are 2 relevant strategies, but also consider fine-tuning a model. Fine-tuning influences the model with additional examples that reflect specific requirements. These examples adjust the pretrained internal weights so that it produces responses consistent with patterns in the training data. […]
Read More →