Prompt engineering is the strategic process of crafting tailored prompts to guide language models, ensuring desired outputs and enabling effective communication between humans and AI, making it vital for achieving precise and contextually appropriate responses.

The workshop will go over the following topics:

• Semantic Role Labeling (SRL) for prompt refinement
• Syntax-driven coherence modeling in prompt construction
• Neural machine translation techniques for prompt optimization
• Contextual embeddings and their role in priming language models
• Transfer learning architectures, particularly transformer-based models
• Language pattern matching algorithms for prompt generation
• Syntactic parsing libraries for prompt analysis
• Fine-tuning methodologies for model adaptation in prompt engineering
• Reinforcement learning approaches for prompt optimization
• Zero-shot transfer learning for prompt generalization
• Ethical considerations in prompt construction, including fairness and interpretability
• Mitigating unintended biases and consequences in prompt engineering