Prompt engineering, a fusion of art and science, is the process of shaping input text entered by us as user to optimize the performance of a given large language model.
Foundation models, boasting billions of parameters and trained on vast datasets, serve various functions including text, code, or image generation, classification, and conversation. Large language models, a subset of these, specialize in text and code tasks. However, there isn’t a singular method to prompt them effectively; rather, there exist multiple approaches to elicit desired outcomes.
From monitoring token usage to striking a balance between intelligence and security, you’ll explore diverse exercises showcasing different techniques, controls, and adjustments to achieve your desired model outputs. By the end of this course, participants will possess a robust understanding of prompt engineering, coupled with practical skills essential for maximizing the performance of large language models.