• Welcome and workshop overview
• What is generative AI?
• History and evolution of generative models
• Key differences between discriminative and generative models
• Current landscape of generative AI technologies
• Real-world applications and impact

• Neural networks primer: essential concepts
• Transformer architecture explained
• Attention mechanisms and their importance
• Training methodologies: supervised, unsupervised, and reinforcement learning
• Understanding parameters, tokens, and context windows
• Brief overview of leading foundation models (GPT, Claude, LLaMA, etc.)

• What is prompt engineering?
• Basic prompt structures and components
• Zero-shot and few-shot prompting
• Chain-of-thought and step-by-step reasoning
• Role-based prompting techniques
• System prompts vs. user prompts

• Hands-on exercises with various LLMs
• Prompt templates and frameworks
• A/B testing different prompt strategies
• Best practices for consistent results
• Common pitfalls and how to avoid them
• Group exercise: solving real-world problems with effective prompts

• Context management strategies
• Recursive prompting techniques
• Retrieval-augmented generation (RAG)
• Prompt chaining and workflows
• Evaluation metrics for prompt effectiveness
• Ethical considerations in prompt design

• Understanding vector embeddings
• How embedding models work
• Text embeddings vs. multimodal embeddings
• Leading embedding models compared (OpenAI, Cohere, BERT, etc.)
• Semantic search implementation
• Visualizing and analyzing embeddings

Building RAG Systems

• Architecture of retrieval-augmented generation
• Document processing and chunking strategies
• Vector databases (Pinecone, Weaviate, Chroma, etc.)
• Similarity search techniques
• Hybrid search approaches
• Evaluation metrics for RAG systems

Practical Workshop: Implementing a RAG System

• Hands-on implementation of a simple RAG application
• Document ingestion and processing pipeline
• Building and querying a vector database
• Integrating search results with LLM generation
• Troubleshooting and optimization techniques
• Group exercise: developing custom RAG solutions for domain-specific use cases

Fine-tuning and Transfer Learning 

• When and why to fine-tune models
• Data preparation and cleaning techniques
• Fine-tuning methodologies and best practices
• Parameter-efficient fine-tuning (PEFT, LoRA, QLoRA)
• Evaluating fine-tuned models
• Cost-benefit analysis of fine-tuning vs. prompt engineering

• Introduction to multimodal AI systems
• Text-to-image models (DALL-E, Midjourney, Stable Diffusion)
• Image-to-text capabilities
• Video generation technologies
• Audio and speech models
• Multimodal prompt engineering techniques

• LLM application architectures
• Model serving infrastructure
• Scaling considerations and optimization
• Monitoring and observability
• Caching strategies
• Cost management and optimization
• Security and privacy considerations

• AI agent frameworks and architectures
• Tool use and function calling
• Planning and reasoning capabilities
• Multi-agent systems
• Memory and context management
• Evaluation frameworks for agent systems

• Recap of key concepts and learnings
• Emerging trends in generative AI
• Resources for continued learning
• Q&A session
• Feedback collection
• Closing remarks