Prompt Engineering and Advanced AI Techniques
Description
Advanced prompt engineering techniques, model interaction strategies, chain-of-thought reasoning, and sophisticated AI application development methods for optimizing AI system performance.
Learning Objectives
Learners will master advanced prompt engineering techniques including chain-of-thought prompting, few-shot learning, prompt optimization, template design, and advanced reasoning strategies. They will understand model interaction patterns, prompt injection prevention, automated prompt generation, and sophisticated AI application development techniques.
Topics (9)
Comprehensive study of prompt architecture, design patterns, optimization techniques, and best practices for creating effective AI instructions.
Detailed study of CoT techniques, reasoning chains, problem decomposition, and structured thinking approaches for AI systems.
Comprehensive coverage of in-context learning, example crafting, demonstration selection, and context window optimization techniques.
Study of prompt templates, systematic design methodologies, pattern libraries, and scalable prompting frameworks for enterprise applications.
Comprehensive study of optimization methodologies, testing frameworks, performance metrics, and continuous improvement approaches for prompt engineering.
Detailed study of conversation flow design, context preservation, memory systems, and dialogue state management for sophisticated AI interactions.
Comprehensive coverage of prompt security threats, injection attacks, safety measures, content filtering, and defensive prompting techniques.
Comprehensive study of industry-specific prompting techniques, domain expertise integration, specialized vocabulary usage, and field-specific optimization strategies.
Study of automated optimization techniques, genetic algorithms for prompts, neural prompt search, and automated improvement systems.