Advanced LLM Prompting Techniques & Adaptation Workshop

target_audience: "Machine learning engineers and data scientists", workshop_duration: "2-day intensive workshop", focus_area: "Fine-tuning and prompt optimization for business applications", technical_level: "Intermediate to advanced"

# Advanced LLM Prompting & Adaptation Workshop ## Executive Summary This intensive 2-day workshop equips ML engineers and data scientists with cutting-edge techniques for optimizing LLM performance in business contexts through advanced prompting strategies and efficient model adaptation. ## Learning Objectives - Master 8+ advanced prompting techniques with 90%+ accuracy improvement - Implement cost-effective fine-tuning strategies reducing inference costs by 40% - Deploy production-ready prompt optimization pipelines - Evaluate and debug LLM outputs using systematic frameworks ## Workshop Modules ### Module 1: Prompt Engineering Fundamentals (3 hours) **Key Concepts:** Role-based prompting, context optimization, output formatting **Hands-on Exercise:** Build a customer service chatbot with structured JSON outputs **Code Example:** ```python def create_customer_service_prompt(query, context): return f"""You are a senior customer service specialist...""" ``` ### Module 2: Advanced Prompting Patterns (4 hours) **Techniques Covered:** - Chain-of-thought with intermediate reasoning steps - Self-consistency prompting for reliability - Tree-of-thoughts for complex problem solving **Real-world Application:** Financial report analysis with multi-step reasoning **Assessment:** Achieve 85%+ accuracy on benchmark reasoning tasks ### Module 3: Model Adaptation Strategies (3 hours) **Focus Areas:** - Parameter-efficient fine-tuning (LoRA, AdaLoRA) - Prompt tuning vs full fine-tuning trade-offs - Domain adaptation with minimal data **Practical Exercise:** Adapt GPT-3.5 for legal document classification **Success Metric:** Reduce training time by 60% while maintaining F1 score >0.9 This comprehensive workshop includes 12 Jupyter notebooks, 15+ Python scripts, and a complete evaluation framework for measuring prompt effectiveness in production environments.