CIS 6930 Spring 26
Data Engineering at the University of Florida
CIS 6930 SP26 - Lecture Reading List
This document maps required and optional readings to each lecture in the course.
Module 1: Foundations (Weeks 1-4)
Week 1: Course Setup
No readings - infrastructure focus
Week 2: Model Context Protocol (MCP)
Lecture: MCP Fundamentals, Building MCP Servers, Multi-agent Pipelines
| Type | Paper | Link | Summary |
|---|---|---|---|
| Required | Model Context Protocol Specification | MCP Docs | Official specification for MCP, covering core concepts, architecture, and protocol design. Essential for understanding how MCP enables communication between LLMs and external tools/data sources. |
| Required | MCP Quickstart Guide | MCP Quickstart | Hands-on guide to building your first MCP server. Covers server creation, tool registration, and client integration. |
| Optional | Building MCP Servers Tutorial | MCP Servers | Detailed tutorial on implementing custom MCP servers with examples. |
| Optional | Multi-Agent Orchestration Patterns | MCP Patterns | Architectural patterns for building multi-agent systems with MCP. |
Week 3: Prompt Engineering Basics
Lecture: Prompt engineering fundamentals, Chain-of-Thought, Structured Outputs
| Type | Paper | Link | Summary |
|---|---|---|---|
| Required | Chain-of-Thought Prompting Elicits Reasoning (Wei et al., 2022) | arXiv:2201.11903 | Demonstrates that including reasoning steps in prompts enables LLMs to solve complex arithmetic, commonsense, and symbolic reasoning tasks. A 540B-parameter model with 8 CoT examples achieved SOTA on math word problems. Foundational work for understanding prompting techniques. |
| Optional | The Prompt Report: A Systematic Survey of Prompting Techniques | arXiv:2406.06608 | Comprehensive taxonomy of 58 prompting techniques and 33 vocabulary terms. Use as a reference guide. If reading, focus on Sections 1-3 (Introduction, Taxonomy, Core Techniques) only - the full survey is extensive. |
| Optional | Large Language Models are Zero-Shot Reasoners (Kojima et al., 2022) | arXiv:2205.11916 | Shows that simply adding “Let’s think step by step” improves reasoning performance dramatically (+61 percentage points on MultiArith). |
| Optional | Tree of Thoughts: Deliberate Problem Solving with LLMs | arXiv:2305.10601 | Extends CoT by allowing exploration of multiple reasoning paths with backtracking. Achieved 74% success on Game of 24 compared to 4% with standard CoT prompting. |
| Optional | Graph of Thoughts: Solving Elaborate Problems with LLMs | arXiv:2308.09687 | Models reasoning as an arbitrary graph with aggregation and refinement. Achieves 62% better quality than ToT on sorting while reducing costs by 31%. |
Discussion: How to read research papers
| Type | Paper | Link | Summary |
|---|---|---|---|
| Required | How to Read a Paper (Keshav) | Classic 3-pass method for reading research papers efficiently: first pass for overview (5 min), second for understanding (1 hour), third for deep comprehension. | |
| Required | LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models (Arora & Dell, 2024) | ACL 2024 Demo | Open-source package making transformer-based record linkage accessible without deep learning expertise. Treats linkage as text retrieval using sentence embeddings. Used as the in-class 3-pass reading exercise. |
Last updated: January 2026 Source: latent.space 2025 reading list + ACL Anthology 2024-2025