Global Agentic AI and LLM Bootcamp May 2025

• Upskill teams in AI-driven automation, natural language processing, and intelligent agents.
• Accelerate AI adoption by providing employees with practical, hands-on training.
• Enhance productivity by leveraging LLMs for knowledge management, customer support, and internal tools.
• Gain strategic insights into how LLMs are shaping industries and how to integrate them effectively.
• Network with top AI professionals to foster collaboration and innovation
  

• Focusing on real-world applications – Go beyond theory with hands-on coding exercises, implementation strategies, and live case studies.
• Offering practical exposure – Work on end-to-end projects, including fine-tuning, inference optimization, and deploying AI-powered agents.
• Providing exclusive industry insights – Learn from AI leaders who have successfully integrated LLMs into their organizations.
• Hands-on with latest tools & frameworks – Gain experience with state-of-the-art libraries, APIs, and architectures that power AI innovation.
  

• Software Engineers & AI Developers – Looking to build and deploy LLM-powered applications.
• Data Scientists & ML Engineers – Interested in fine-tuning and optimizing models for efficiency.
• Startup Founders & Tech Executives – Seeking to integrate AI into products and services.
• Researchers & Academics – Exploring the frontiers of language models and agentic AI.
• Corporate Leaders & Product Managers – Wanting to understand the strategic impact of AI on business operations
  

Conference Location
SpringHill Suites by Marriott, 46333 Fremont Blvd, Fremont, CA 94538 ( Map )

Each student should bring their own 64bit laptop ( Windows 7/8 and Mac, Virtualization Enabled, Minimum 8GB Ram and Free 25GB-50GB hard disk ) with administrative privileges and wireless connectivity. If you have AMD laptop, it should be AMD-V enabled. If you have Intel laptop or Mac, it should support Intel-VTx. An extra USB drive of 16gb minimum will be handy if you want to use your personal USB drive for all files and images.

No refunds will be given for cancellations

If you have any questions concerning Global LLM and Agentic AI Bootcamp, please do not hesitate to contact or Call 408-400-3769

NOTE: Agenda and speakers subject to change without notice


Day 1: Foundations of Generative AI

Morning Session (9:00 AM - 12:00 PM)

Introduction to Generative AI (9:00 - 10:15)

- 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

Coffee Break (10:15 - 10:30)

Foundation Models and Architecture (10:30 - 12:00)

- 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.)

Lunch Break (12:00 - 1:00 PM)

Afternoon Session (1:00 PM - 5:00 PM)

Prompt Engineering Fundamentals (1:00 - 3:00)

- 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

Coffee Break (3:00 - 3:15)

Practical Workshop: Basic Prompt Engineering (3:15 - 5:00)

- 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

Day 2: Advanced Techniques and Implementations

Morning Session (9:00 AM - 12:00 PM)

Advanced Prompt Engineering (9:00 - 10:30)

- Context management strategies

- Recursive prompting techniques

- Retrieval-augmented generation (RAG)

- Prompt chaining and workflows

- Evaluation metrics for prompt effectiveness

- Ethical considerations in prompt design

Coffee Break (10:30 - 10:45)

Embedding Models Deep Dive (10:45 - 12:00)

- 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

Lunch Break (12:00 - 1:00 PM)

Afternoon Session (1:00 PM - 5:00 PM)

Building RAG Systems (1:00 - 3:00)

- 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

Coffee Break (3:00 - 3:15)

Practical Workshop: Implementing a RAG System (3:15 - 5:00)

- 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


Day 3: Advanced Topics and Production Deployment

Morning Session (9:00 AM - 12:00 PM)

Fine-tuning and Transfer Learning (9:00 - 10:30)

- 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

Coffee Break (10:30 - 10:45)

Multimodal AI and Beyond Text (10:45 - 12:00)

- 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

Lunch Break (12:00 - 1:00 PM)

Afternoon Session (1:00 PM - 5:00 PM)

Production Deployment and MLOps (1:00 - 2:30)

- LLM application architectures

- Model serving infrastructure

- Scaling considerations and optimization

- Monitoring and observability

- Caching strategies

- Cost management and optimization

- Security and privacy considerations

Coffee Break (2:30 - 2:45)

AI Agents and Autonomous Systems (2:45 - 4:00)

- 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

Workshop Conclusion and Future Directions (4:00 - 5:00)

- Recap of key concepts and learnings

- Emerging trends in generative AI

- Resources for continued learning

- Q&A session

- Feedback collection

- Closing remarks

Pre-Workshop Requirements

Technical Requirements

- Basic programming knowledge (Python preferred)

- Laptop with internet connection

- Development environment setup (instructions will be provided)

- API keys for various AI services (optional, but recommended)

Recommended Pre-Reading

- Introduction to machine learning concepts

- Basic neural network principles

- Python programming fundamentals (if needed)

Post-Workshop Resources

- Workshop slides and materials

- Code repositories and examples

- Recommended reading list

- Community forum access

- Certificate of completion

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