Generative AI for Analytics (BADM 5XX or MSBAi550A)
Program-level details: See program/CURRICULUM.md
Status: TBD (pending approval)
| Credits: 2 | Term: Spring 2027 (Weeks 5-8, concurrent with BADM 558 second half) |
Course Vision
Students move beyond prompt engineering to build AI-powered analytics systems. The course covers LLM fundamentals, Retrieval-Augmented Generation (RAG) pipelines, and agentic AI patterns — equipping graduates to build internal AI tools, not just use them. By course end, students can design and implement RAG systems and agent-based workflows for real analytics problems.
Learning Outcomes (L-C-E Framework)
Literacy:
- L1: Explain how large language models work (transformer architecture, token prediction, context windows)
- L2: Understand RAG architecture (retrieval, embedding, generation) and when to use it vs. fine-tuning
- L3: Recognize AI governance frameworks (NIST AI RMF) and ethical issues in AI deployment
Competency:
- C1: Build a RAG pipeline using LangChain, vector databases, and embedding models
- C2: Implement agentic AI patterns (function calling, tool use) for analytics workflows
- C3: Apply prompt engineering techniques for data analysis, code generation, and report writing
- C4: Audit AI outputs for accuracy, bias, and ethical concerns
Expertise:
- E1: Design AI-augmented analytics workflows combining RAG, agents, and human judgment
- E2: Evaluate trade-offs between RAG, fine-tuning, and prompt engineering for a given problem
- E3: Build production-ready AI systems with appropriate governance and documentation
Week-by-Week Breakdown
| Week | Topic | Activities | Assessment |
|---|---|---|---|
| 1 | LLM fundamentals + prompt engineering | Transformer architecture overview, prompt patterns for analytics (chain-of-thought, few-shot), AI for SQL/Python generation | Prompt engineering exercises |
| 2 | RAG implementation | LangChain fundamentals, document chunking strategies, embedding models (OpenAI Embeddings API), vector databases (ChromaDB/Pinecone), retrieval evaluation | Project 1: RAG pipeline |
| 3 | Agentic AI patterns + governance | Function calling, tool use, multi-agent intro, orchestration patterns, NIST AI RMF overview, AI governance basics | Agentic AI lab |
| 4 | Capstone project + ethics | Build RAG or agent-based analytics workflow, ethics case study, responsible AI checklist | Project 2: Agentic capstone + oral defense |
Projects (2 major, both individual — 4-week course)
Project 1: RAG Pipeline (Weeks 1-2, Individual, 40% of grade)
- Task: Build a RAG system that answers business questions from a document corpus
- Deliverables:
- Document ingestion pipeline (chunking strategy, embedding model selection)
- Vector database setup (ChromaDB or Pinecone)
- LangChain retrieval chain with query interface
- Evaluation: retrieval accuracy on 10+ test questions
- Write-up: architecture decisions, trade-offs, limitations
- GitHub repo with documented code
Project 2: Agentic Capstone (Weeks 3-4, Individual, 50% of grade)
- Task: Design an agent-based analytics workflow for a business problem
- Deliverables:
- Agent system using function calling / tool use patterns
- Integration with at least one external tool (database, API, or analytics library)
- AI governance documentation (model card, usage guidelines, risk assessment)
- Ethics checklist: bias audit, limitation documentation
- Oral defense: 10-min presentation + Q&A (included in Project 2 grade)
- GitHub repo with code + documentation
Studio Participation: 10% of grade
Assessment Summary
| Component | Weight | Notes |
|---|---|---|
| Project 1 (RAG Pipeline) | 40% | Weeks 1-2, individual |
| Project 2 (Agentic Capstone) | 50% | Weeks 3-4, individual, includes oral defense |
| Studio participation | 10% | Weekly attendance |
No traditional exam. Project-based with AI systems focus.
Rubric (5 dimensions)
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| RAG Implementation | Well-architected pipeline, strong retrieval accuracy, justified chunking/embedding choices | Functional pipeline, adequate accuracy | Basic implementation, poor retrieval quality |
| Agentic Design | Sophisticated agent patterns, effective tool use, handles edge cases | Functional agent, basic tool integration | Minimal agent functionality |
| AI Governance | Comprehensive model card, risk assessment, NIST alignment | Adequate governance documentation | Minimal or missing governance |
| Code Quality | Production-ready, well-documented, tested | Functional with minor issues | AI code has problems |
| Oral Defense | Explains architecture clearly, handles questions confidently, articulates trade-offs | Adequate explanation, answers most questions | Cannot explain choices or struggles with Q&A |
Technology Stack
- AI Tools: Claude (API + web), ChatGPT (Plus or API), GitHub Copilot
- RAG Framework: LangChain (required)
- Vector Databases: ChromaDB (primary), Pinecone (alternative)
- Embeddings: OpenAI Embeddings API
- Environment: Jupyter Notebooks with AI integration
- Governance: NIST AI RMF reference framework
Prerequisites
- Completion of at least 2 core MSBAi courses (familiarity with analytics workflow)
Last Updated: February 2026