AI for Software Engineering

How to use AI to do software engineering (mid 2026 version!)

Learning Outcomes

• be able to explain components of the AI4SE lifecycle:
  • prompting
  • context and RAG
  • tool use with MCP and Skills
  • evals
  • models and pitfalls
• use a modern LLM for software development
• contextualize the places AI can assist in, replace, and/or hinder good SE practices and good SE systems.

Before Class

Lectures

• AI4SE • video part 1 • video part 2

Readings

1. Simon Willison: How I use LLMs
2. Simon Willison: 2025 in LLMs
3. Fowler: Structured PDD;
4. Your LLM … Writes Plausible Code

In the previous class we installed a common set of AI tools. Make sure that this works for you on a machine you can bring to class.

In Class

Slides

• In-class notes

Data and code

• Using Gemini to do “data science” (file is AI generated)
• Promptfoo and promptfoo config file

Optional Readings and Activities (so many … send more!)

• Fowler: Patterns for GenAI series
  
• Kalliamvakou: quantifying GitHub Copilot’s impact on developer productivity and happiness
• Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Willison, LLM tutorial - I drew on this a lot for my notes.
• Tim Bray, Claude and Go and pointers
• Thoughtworks Consultants on what works
• Kent Beck on building a library
• helpful prompt suggestions to fix Claude mistakes
• on the transition from craft to oversight in SE
• maybe AI isn’t as great as it is made out to be?
• Guy who has been around protocols for a while talks MCP.
• Context engineering summary
• Model Context Protocol
• Spec for MCP
• Best Practices for Agentic Coding
• Contexts
• AI at Shopify
• Shen and Tamkin 2025, “How AI Impacts Skill Formation”