Course Schedule

Week-by-Week Breakdown

This schedule provides an overview of the topics covered each week. Specific readings and assignments will be announced in class.

Week 1: Introduction to Large Language Models

Topics:

  • Course overview and objectives
  • History and evolution of language models
  • Transformer architecture basics
  • Current state-of-the-art LLMs

Materials:

  • Lecture slides: Introduction to LLMs
  • Reading: “Attention Is All You Need” (Vaswani et al., 2017)

Lab:

  • Setting up your development environment
  • First experiments with LLM APIs

Week 2: Understanding LLM Capabilities and Limitations

Topics:

  • What LLMs can and cannot do
  • Emergent abilities in large models
  • Common failure modes and pitfalls
  • Context windows and token limits

Materials:

  • Lecture slides: LLM Capabilities
  • Reading: Selected papers on LLM evaluation

Assignment 1 Due: Basic LLM interaction exercises

Week 3: Prompt Engineering Fundamentals

Topics:

  • Prompt design principles
  • Zero-shot, few-shot, and many-shot learning
  • Instruction following
  • Best practices for clear prompts

Materials:

  • Lecture slides: Prompt Engineering
  • Interactive prompt engineering workshop

Lab:

  • Hands-on prompt design challenges

Week 4: Advanced Prompting Techniques

Topics:

  • Chain-of-thought reasoning
  • Self-consistency
  • Tree of thoughts
  • Prompt optimization strategies

Materials:

  • Lecture slides: Advanced Prompting
  • Reading: Chain-of-Thought papers

Assignment 2 Due: Prompt engineering project

Week 5: Introduction to AI Agents

Topics:

  • What is an AI agent?
  • Agent architectures (ReAct, Plan-and-Execute)
  • Reasoning and action cycles
  • Tool use and function calling

Materials:

  • Lecture slides: AI Agents
  • Reading: ReAct and agent papers

Lab:

  • Building your first agent

Week 6: Memory and Context Management

Topics:

  • Short-term and long-term memory
  • Vector databases and embeddings
  • Context window optimization
  • Retrieval-Augmented Generation (RAG)

Materials:

  • Lecture slides: Memory Systems
  • Demo: RAG implementation

Assignment 3 Due: Simple agent implementation

Week 7: Multi-Agent Systems

Topics:

  • Agent communication protocols
  • Coordination and collaboration
  • Specialization and role assignment
  • Consensus and decision-making

Materials:

  • Lecture slides: Multi-Agent Systems
  • Reading: Multi-agent collaboration papers

Lab:

  • Building collaborative agent systems

Midterm Project Due: Agent system design and implementation

Week 8: Advanced Agent Capabilities

Topics:

  • Self-reflection and improvement
  • Iterative refinement
  • Planning and goal decomposition
  • Error handling and recovery

Materials:

  • Lecture slides: Advanced Agents
  • Case studies of production agent systems

Week 9: Integration with External Systems

Topics:

  • API integration
  • Database connections
  • Web scraping and data collection
  • Real-time data streams

Materials:

  • Lecture slides: System Integration
  • Practical examples and demos

Assignment 4 Due: Multi-agent system

Week 10: Agent Orchestration and Frameworks

Topics:

  • LangChain, AutoGen, and other frameworks
  • Workflow design and orchestration
  • Debugging and monitoring
  • Performance optimization

Materials:

  • Lecture slides: Frameworks
  • Hands-on framework tutorials

Lab:

  • Framework exploration and comparison

Week 11: Evaluation and Testing

Topics:

  • Metrics for agent performance
  • Benchmark datasets
  • Human evaluation methods
  • A/B testing for agents

Materials:

  • Lecture slides: Evaluation
  • Reading: LLM evaluation papers

Assignment 5 Due: Integration project

Week 12: Safety, Ethics, and Responsible AI

Topics:

  • AI safety principles
  • Bias and fairness
  • Privacy considerations
  • Guardrails and content filtering
  • Ethical decision-making

Materials:

  • Lecture slides: AI Safety and Ethics
  • Discussion: Real-world case studies

Lab:

  • Implementing safety measures

Week 13: Production Deployment

Topics:

  • Deployment strategies
  • Scalability and performance
  • Cost optimization
  • Monitoring and maintenance

Materials:

  • Lecture slides: Deployment
  • Guest speaker: Industry perspective

Week 14: Future Directions and Research

Topics:

  • Current research frontiers
  • Open challenges in agentic AI
  • Future capabilities and risks
  • Career paths in AI

Materials:

  • Lecture slides: Future of Agentic AI
  • Student project presentations

Week 15: Final Project Presentations

Topics:

  • Student project demonstrations
  • Peer feedback and discussion
  • Course wrap-up

Final Project Due: Complete agentic LLM application

Important Dates

  • Week 1: Course begins
  • Week 7: Midterm project due
  • Week 15: Final project presentations and submission

Note: This schedule is subject to change based on class progress and guest speaker availability. Any changes will be announced in advance.

Last updated: February 2026