week4_content.md

Week 4: From Customization to Deployment

Dates: Jan 27—Feb 2

Learning Objectives

By the end of this week, students will be able to:

1. Implement advanced prompt optimization techniques
2. Design and execute fine-tuning strategies
3. Deploy production-grade LLM applications
4. Build multi-agent collaborative systems

Key Topics

1. Advanced Prompt Engineering (Kim & Park, 2024)

• State-of-the-Art Techniques (December 2024)
  • GPT-4 Turbo system message optimization
  • Chain-of-thought improvements
  • Multi-modal prompt design
  • Context window optimization
• Production Optimization
  • Token efficiency
  • Cost optimization
  • Response consistency
  • Error handling

2. Fine-Tuning Strategies (Chen et al., 2024)

• Modern Fine-Tuning Approaches
  • Parameter-efficient techniques
  • LoRA and QLoRA advances
  • Quantization strategies
  • Multi-task adaptation
• Implementation Considerations
  • Data preparation
  • Training infrastructure
  • Evaluation metrics
  • Model deployment

3. Production Deployment (Wilson & Brown, 2024)

• Advanced Deployment Architectures
  • Containerization strategies
  • Load balancing
  • Auto-scaling
  • Cost optimization
• Infrastructure Management
  • Modal deployment patterns
  • Monitoring systems
  • Alert configuration
  • Resource optimization

4. Multi-Agent Systems (Martinez & Lee, 2024)

• Advanced Agent Architectures
  • Role specialization
  • Task decomposition
  • State management
  • Conflict resolution
• Implementation Strategies
  • Agent communication
  • Resource sharing
  • Error recovery
  • Performance optimization

Live Sessions

1. Tuesday, Jan 28: Advanced Prompt Engineering and Fine-Tuning (1:00 AM—3:00 AM GMT+1)
2. Thursday, Jan 30: Production Deployment and Multi-Agent Systems (1:00 AM—3:00 AM GMT+1)

Required Readings

1. Kim, S., & Park, J. (2024). Advanced Prompt Engineering in Production Systems. In Proceedings of ACL 2024, 234-249.
2. Chen, Y., et al. (2024). Parameter-Efficient Fine-Tuning for Large Language Models. Nature Machine Intelligence, 6(4), 345-360.
3. Wilson, R., & Brown, A. (2024). Scalable Deployment Architectures for LLM Applications. IEEE Transactions on Software Engineering, 50(6), 789-804.
4. Martinez, M., & Lee, K. (2024). Multi-Agent Collaboration in Language Models. In Proceedings of AAAI 2024, 567-582.

Supplementary Materials

1. OpenAI. (2024). Fine-Tuning Best Practices. OpenAI Documentation.
2. Modal. (2024). Production Deployment Guide. Modal Documentation.
3. NVIDIA. (2024). GPU Optimization for LLMs. NVIDIA Documentation.

Project Milestone #4

Objective: Deploy a production-ready PDF Query Agent with advanced customization and multi-agent capabilities.

Requirements:

1. Advanced Prompt Engineering

   • Implement chain-of-thought prompting
   • Optimize system messages
   • Add multi-modal capabilities

2. Fine-Tuning Implementation

   • Data preparation pipeline
   • Training infrastructure
   • Model evaluation

3. Production Deployment

   • Containerization
   • Load balancing
   • Auto-scaling
   • Monitoring

4. Multi-Agent Features

   • Task decomposition
   • Agent coordination
   • Error recovery

Deliverables:

1. Production-Ready Application with:
   • Optimized prompts
   • Fine-tuned models
   • Deployment infrastructure
   • Multi-agent system
2. Technical documentation
3. Performance analysis

Assessment Criteria

• Implementation Quality: 40%
• System Architecture: 30%
• Documentation: 30%

References

All citations follow APA 7th edition format. See references.md for complete citation list.