Prompt Engineering and Optimization Techniques
Description
Advanced techniques for designing, optimizing, and refining prompts to achieve desired outputs from generative AI models.
Learning Objectives
Learners will master the art and science of prompt engineering including various prompting techniques, optimization strategies, and advanced methods like chain-of-thought, few-shot learning, and retrieval-augmented generation to maximize the effectiveness of AI model outputs.
Topics (8)
Advanced prompting method that guides models through step-by-step reasoning processes, improving accuracy in complex problem solving and logical reasoning tasks.
Strategies for providing effective examples within prompts to guide model behavior, including example selection, formatting, and balancing techniques for optimal learning.
Sophisticated prompting strategies including tree-of-thought, self-refine, directional-stimulus, and other cutting-edge techniques for complex AI interactions.
Methodologies for iterative prompt improvement including testing frameworks, performance metrics, and systematic optimization approaches for consistent results.
Strategies for maintaining context, managing conversation memory, and using contextual priming to improve model consistency and relevance across extended interactions.
Specialized prompting approaches for different use cases including code generation, creative writing, data analysis, and professional communication.
Understanding potential security vulnerabilities in prompting including prompt injection, jailbreaking prevention, and implementing safety guardrails in prompt design.
Foundation concepts of prompt engineering including prompt anatomy, clear instruction formulation, context setting, and basic formatting techniques for optimal model response.