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Computer Science
Artificial Intelligence
Prompt Engineering
1. Introduction to Prompt Engineering
2. Fundamentals of Large Language Models
3. Core Components of Effective Prompts
4. Basic Prompting Techniques
5. Crafting High-Quality Prompts
6. Advanced Prompting Strategies
7. Prompt Engineering Workflow
8. Task-Specific Applications
9. Security and Safety Considerations
10. Performance Evaluation and Optimization
11. Advanced and Emerging Topics
6.
Advanced Prompting Strategies
6.1.
Chain-of-Thought Prompting
6.1.1.
Step-by-Step Reasoning Encouragement
6.1.2.
Zero-Shot CoT Implementation
6.1.3.
Few-Shot CoT with Examples
6.1.4.
Reasoning Path Visualization
6.1.5.
Intermediate Step Validation
6.2.
Self-Consistency Approaches
6.2.1.
Multiple Path Generation
6.2.2.
Output Sampling Strategies
6.2.3.
Majority Voting Implementation
6.2.4.
Result Aggregation Methods
6.2.5.
Confidence Assessment
6.3.
Tree of Thoughts Framework
6.3.1.
Systematic Path Exploration
6.3.2.
Branching Strategy Design
6.3.3.
Pruning Technique Implementation
6.3.4.
Self-Evaluation Methods
6.3.5.
Backtracking Mechanisms
6.3.6.
Decision Tree Construction
6.4.
Generated Knowledge Prompting
6.4.1.
Fact Generation Strategies
6.4.2.
Knowledge Integration Techniques
6.4.3.
Multi-Stage Prompting
6.4.4.
Information Validation Methods
6.4.5.
Context Enrichment Approaches
6.5.
ReAct Framework
6.5.1.
Reasoning and Action Interleaving
6.5.2.
Reasoning Step Design
6.5.3.
Action Step Implementation
6.5.4.
Tool Integration Methods
6.5.5.
External API Interaction
6.5.6.
Agent Behavior Modeling
6.6.
Retrieval Augmented Generation
6.6.1.
External Knowledge Integration
6.6.2.
Retrieval Step Optimization
6.6.3.
Generation Step Enhancement
6.6.4.
Knowledge Base Selection
6.6.5.
Relevance Filtering
6.6.6.
Information Synthesis
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5. Crafting High-Quality Prompts
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7. Prompt Engineering Workflow