Students will learn prompt engineering techniques and methods to apply to AI text to text LLM.
Abstract
Methods such as conditional logic, tokenization, prompt libraries, pre-processing, domain-specific language, syntactic cues, parallel prompts, intentional branching, identity assignation, and multi-turn refinement will be introduced, explained and modeled during this resource.
Title of the resource
Text to Text Prompt Engineering Intensive
Resource type
Hosted Resource
Authors, editors and contributors
Emily Genatowski
Topics (keywords)
AI, Large Language Models
Learning outcomes
Students will learn prompt engineering techniques and methods to apply to AI text to text LLM.
Abstract
Methods such as conditional logic, tokenization, prompt libraries, pre-processing, domain-specific language, syntactic cues, parallel prompts, intentional branching, identity assignation, and multi-turn refinement will be introduced, explained and modeled during this resource.