2025-09-14 00:05:00

AI Engineering Curriculum - Professor’s Teaching Manual

Course Overview

This comprehensive manual provides professors with everything needed to deliver a cutting-edge AI Engineering curriculum to undergraduate students pursuing degrees in AI/ML. The curriculum is structured into four progressive levels, each building upon the previous to create a complete educational journey from AI fundamentals to enterprise-scale deployment.

Course Philosophy

Core Principles

1. Practical First: Every concept is immediately applied through hands-on coding
2. Current Technology: All content reflects 2024-2025 state-of-the-art practices
3. Production Focus: Students learn to build systems that work in the real world
4. Progressive Complexity: Each level scaffolds knowledge appropriately
5. Industry Alignment: Skills directly transfer to professional environments

Course Structure

Duration

• Full Program: 32 weeks (2 semesters)
• Each Level: 8 weeks
• Weekly Commitment: 6 hours lecture + 9 hours lab/project work

Prerequisites

• Level 1: Basic programming (Python), introductory CS concepts
• Level 2: Completion of Level 1 or equivalent experience
• Level 3: Completion of Level 2, understanding of software engineering principles
• Level 4: Completion of Level 3, basic knowledge of distributed systems

Level-by-Level Teaching Guide

Level 1: Using AI (Weeks 1-8)

Learning Objectives

Students will master the fundamentals of interacting with AI systems, understanding their capabilities and limitations.

Week-by-Week Breakdown

Weeks 1-2: Prompt Engineering Fundamentals

• Lecture Topics:
  • Introduction to language models and tokenization
  • Zero-shot vs few-shot learning
  • Chain-of-thought prompting
  • Advanced techniques (ToT, GoT, ThoT)
• Lab Activities:
  • Prompt engineering challenges
  • Comparing prompt strategies
  • Building a prompt library
• Key Takeaways:
  • Prompts are programs for AI
  • Structure and clarity matter
  • Different tasks require different approaches

Weeks 3-4: API Integration

• Lecture Topics:
  • Overview of major AI providers
  • Authentication and rate limiting
  • Cost management strategies
  • Error handling patterns
• Lab Activities:
  • Integrate multiple AI APIs
  • Implement retry logic and caching
  • Build a cost tracking system
• Key Takeaways:
  • APIs are the interface to AI
  • Cost awareness is critical
  • Reliability requires robust error handling

Weeks 5-6: Practical Applications

• Lecture Topics:
  • Building AI-powered applications
  • User interface considerations
  • Performance optimization
  • Security best practices
• Lab Activities:
  • Build a document summarizer
  • Create a chatbot interface
  • Implement response streaming
• Project: Complete AI application with proper architecture

Weeks 7-8: Evaluation and Best Practices

• Lecture Topics:
  • Evaluation metrics for AI outputs
  • A/B testing frameworks
  • User feedback integration
  • Ethical considerations
• Lab Activities:
  • Implement evaluation pipelines
  • Conduct A/B tests
  • Analyze user feedback
• Assessment: Final project presentation and code review

Level 2: Integrating AI (Weeks 9-16)

Learning Objectives

Students will build sophisticated AI systems using RAG, vector databases, and intelligent agents.

Week-by-Week Breakdown

Weeks 9-10: RAG Fundamentals

• Lecture Topics:
  • RAG architecture and components
  • Document processing strategies
  • Chunking algorithms
  • Retrieval techniques
• Lab Activities:
  • Implement document chunking
  • Build retrieval pipeline
  • Compare chunking strategies

Weeks 11-12: Vector Databases and Embeddings

• Lecture Topics:
  • Understanding embeddings
  • Vector database comparison
  • Similarity metrics
  • Hybrid search techniques
• Lab Activities:
  • Work with multiple vector databases
  • Implement semantic search
  • Build hybrid search system

Weeks 13-14: Intelligent Agents

• Lecture Topics:
  • Agent architectures
  • Tool use and function calling
  • Planning and reasoning
  • Multi-agent systems
• Lab Activities:
  • Build ReAct agent
  • Implement tool integration
  • Create agent orchestration

Weeks 15-16: Optimization Techniques

• Lecture Topics:
  • Caching strategies
  • Batch processing
  • Cost optimization
  • Performance monitoring
• Lab Activities:
  • Implement caching layers
  • Optimize API usage
  • Build monitoring dashboard
• Project: Production-ready RAG system with agents

Level 3: Engineering AI Systems (Weeks 17-24)

Learning Objectives

Students will transform prototypes into production-ready systems with fine-tuning, safety, and evaluation.

Week-by-Week Breakdown

Weeks 17-19: Fine-Tuning and Customization

• Lecture Topics:
  • Fine-tuning paradigms
  • LoRA and QLoRA techniques
  • Instruction tuning
  • RLHF principles
• Lab Activities:
  • Fine-tune models with QLoRA
  • Implement instruction tuning
  • Compare fine-tuning approaches

Weeks 20-21: Safety and Compliance

• Lecture Topics:
  • Content filtering systems
  • Input/output validation
  • Adversarial testing
  • Compliance requirements
• Lab Activities:
  • Build safety guardrails
  • Implement PII detection
  • Conduct adversarial testing

Weeks 22-23: Multi-Model Architectures

• Lecture Topics:
  • Ensemble systems
  • Model routing strategies
  • Cascade architectures
  • Specialized models
• Lab Activities:
  • Build model router
  • Implement cascade system
  • Create ensemble predictions

Week 24: Evaluation Frameworks

• Lecture Topics:
  • Automated metrics
  • Human evaluation
  • A/B testing at scale
  • Performance benchmarking
• Lab Activities:
  • Implement evaluation suite
  • Design human evaluation
  • Create benchmark tests
• Project: Enterprise-grade AI system with full safety and evaluation

Level 4: Optimizing AI at Scale (Weeks 25-32)

Learning Objectives

Students will master enterprise-scale deployment, optimization, and governance of AI systems.

Week-by-Week Breakdown

Weeks 25-26: Distributed Inference

• Lecture Topics:
  • Distributed frameworks (vLLM, Ray)
  • Tensor and pipeline parallelism
  • Load balancing strategies
  • Auto-scaling architectures
• Lab Activities:
  • Deploy with vLLM
  • Implement Ray Serve
  • Build load balancer

Weeks 27-28: Memory and Context Management

• Lecture Topics:
  • Context window optimization
  • Memory-efficient loading
  • Quantization techniques
  • Caching strategies
• Lab Activities:
  • Implement context compression
  • Apply quantization
  • Optimize memory usage

Weeks 29-30: Cost Optimization

• Lecture Topics:
  • Dynamic model selection
  • Cost tracking systems
  • Budget management
  • ROI analysis
• Lab Activities:
  • Build cost optimizer
  • Implement cascading
  • Create cost dashboard

Weeks 31-32: Privacy and Governance

• Lecture Topics:
  • Privacy-preserving techniques
  • Regulatory compliance
  • Audit systems
  • Data governance
• Lab Activities:
  • Implement PII protection
  • Build audit system
  • Ensure compliance
• Project: Production deployment with full optimization and governance

Teaching Methodologies

Lecture Strategies

Interactive Demonstrations

• Live coding during lectures
• Real-time API interactions
• Error debugging sessions
• Performance profiling

Case Studies

• Industry examples
• Failure analysis
• Success stories
• Ethical dilemmas

Guest Speakers

• Industry practitioners
• Researchers
• Open-source contributors
• Startup founders

Lab Structure

Hands-On Learning

• Pair programming sessions
• Code reviews
• Debugging workshops
• Performance optimization

Progressive Projects

• Start with simple implementations
• Add complexity incrementally
• Refactor for production
• Deploy to cloud

Assessment Methods

Continuous Assessment (60%)

• Weekly lab assignments (20%)
• Code quality and documentation (15%)
• Peer reviews (10%)
• Class participation (15%)

Project Work (40%)

• Level projects (4 × 10% each)
• Project includes:
  • Implementation
  • Documentation
  • Presentation
  • Peer evaluation

Resource Requirements

Technical Infrastructure

Hardware

• Minimum: 8GB RAM, quad-core CPU
• Recommended: 16GB RAM, GPU access
• Cloud Credits: $100/student for API access

Software

• Python 3.9+
• VS Code or PyCharm
• Git and GitHub
• Docker (Level 3+)

API Access

• OpenAI API ($50 credits)
• Anthropic API ($30 credits)
• Hugging Face Pro ($10/month)
• Vector database trials

Learning Materials

Required Textbooks

• None (all materials provided in curriculum)

Supplementary Resources

• Research papers (provided)
• Documentation links
• Video tutorials
• Code repositories

Common Challenges and Solutions

Challenge 1: API Costs

Problem: Students exhausting API credits Solution:

• Implement strict rate limiting
• Use caching aggressively
• Provide mock API for testing
• Start with smaller models

Challenge 2: Technical Complexity

Problem: Students overwhelmed by complexity Solution:

• Break into smaller modules
• Provide starter code
• Pair stronger with struggling students
• Extra office hours

Challenge 3: Rapid Technology Changes

Problem: Content becoming outdated Solution:

• Update examples quarterly
• Focus on principles over tools
• Teach adaptation skills
• Use version pinning

Challenge 4: Evaluation Consistency

Problem: Subjective grading of AI outputs Solution:

• Clear rubrics
• Automated testing where possible
• Peer evaluation
• Multiple graders

Best Practices for Professors

Preparation

1. Test all code examples before class
2. Have backup plans for API failures
3. Prepare additional challenges for advanced students
4. Create solution repositories

During Class

1. Start with recap of previous session
2. Use real-world examples
3. Encourage questions
4. Live debug issues

After Class

1. Provide detailed feedback
2. Share additional resources
3. Monitor student progress
4. Adjust pace as needed

Industry Connections

Partnership Opportunities

• Internship programs
• Capstone projects
• Guest lectures
• Hackathons

Career Preparation

• Resume building with AI projects
• Interview preparation
• Portfolio development
• Network building

Evaluation Rubrics

Code Quality (25%)

• Excellent (90-100%): Clean, well-documented, efficient
• Good (70-89%): Works correctly, some optimization needed
• Satisfactory (50-69%): Basic functionality, needs improvement
• Needs Work (<50%): Major issues or incomplete

Problem Solving (25%)

• Excellent: Creative solutions, handles edge cases
• Good: Solid approach, most cases handled
• Satisfactory: Basic solution works
• Needs Work: Incorrect approach

Documentation (25%)

• Excellent: Comprehensive, clear, includes examples
• Good: Good coverage, mostly clear
• Satisfactory: Basic documentation present
• Needs Work: Insufficient documentation

Testing (25%)

• Excellent: Comprehensive tests, good coverage
• Good: Key functionality tested
• Satisfactory: Basic tests present
• Needs Work: Little to no testing

Continuous Improvement

Student Feedback

• Mid-semester evaluations
• End-of-course surveys
• Focus groups
• Alumni feedback

Curriculum Updates

• Quarterly technology review
• Annual major revision
• Industry advisory board
• Research integration

Professional Development

• Attend AI conferences
• Complete online courses
• Industry partnerships
• Research collaboration

Support Resources

For Professors

• Teaching assistant guidelines
• Grading automation scripts
• Lecture slide templates
• Lab setup scripts

For Students

• Office hours schedule
• Tutoring resources
• Study groups
• Online forums

Conclusion

This curriculum prepares students for the rapidly evolving field of AI engineering. By focusing on practical skills, current technology, and production readiness, graduates will be well-equipped for careers in AI development. The progressive structure ensures that all students, regardless of initial skill level, can succeed and excel.

Remember: The goal is not just to teach students how to use AI, but to empower them to build the AI systems of the future. Focus on principles that will endure even as specific technologies evolve.

Appendices

A. Setup Scripts

Available in /resources/setup/

B. Solution Repositories

Access restricted to instructors

C. Lecture Slides Template

Available in /resources/templates/

D. Industry Contact List

Available upon request

E. Research Paper Collection

Updated quarterly in /resources/papers/

2025-09-14 00:05:00