Poor documentation acts like technical debt with interest, slowing down onboarding, causing miscommunication, and creating team friction.
Clean code explains the implementation 'how' but documentation must explain the business 'why' for different audiences like teammates, product managers, and administrators.
Large language models are effective at documentation because code has a defined structure and their training data includes vast amounts of public code repositories.
AI models can hallucinate or fail to grasp complex dependencies in large codebases, requiring careful tooling and prompting to be effective.
An LLM can analyze a C# API controller with bad naming conventions and automatically generate descriptive XML comments explaining the intent of each method.
A demonstration shows how an LLM can analyze a confusing block of code and correctly identify it as the Easter Sunday calculation algorithm.
AI can create comprehensive technical documentation in Markdown and generate client-side API usage examples in various languages like cURL, Python, and JavaScript.
Treat AI-generated content like a draft that needs review, ensure code quality to avoid 'garbage in, garbage out,' and automate updates via your CI/CD pipeline.
The goal of AI in documentation is not to replace developers but to liberate them from repetitive tasks, making documentation a team strength.
msg
Ismaning, Germany
31:39Rainer Stropek
15:26Gregor Schumacher, Sujay Joshy & Marcel Gocke
04:17Robert Werner
25:25Luise Freese
28:07Yan Cui
32:28Hugo Guerrero
29:18Markus Harrer
26:54Duan Lightfoot
Jobs that call for the skills explored in this talk.
Collaboration Betters The World GmbH
Codework
Barcelona, Spain
CommandLink
