sayzardDerrick Choi (@derrickcchoi)
@OpenAI Codex 베스트 프랙티스 관련 글에 대한 피드백 감사 인사와 함께 해당 글이 OpenAI 개발자 문서에 정식으로 게시되었음을 알리는 안내 트윗입니다. 개발자들이 참고할 수 있도록 베스트 프랙티스가 문서화되어 배포되었다는 점에서 실무 참고용으로 유용합니다.
Miguel Afonso Caetano"What are docs for AI agents? How are they different than internal eng docs? Do we really have to maintain the agent docs and eng docs as separate docs sets? This page contains my notes on these questions.
Scope:
I work on developer docs i.e. docs for software engineers. I don’t know how relevant AI agents are for technical writers in other industries or domains.
I’m thinking specifically about docs for AI agents. I’m not sure that an all-encompassing “docs for AI best practices” exists. The way that we optimize docs for RAG-based chatbots (for example) is probably different than the way we optimize docs for AI agents."
https://technicalwriting.dev/ai/agents/
#TechnicalWriting #TechnicalCommunication #SoftwareDocumentation #AI #GenerativeAI #AIAgents #DocsForAIAgents #Chatbots #DeveloperDocs #SoftwareDevelopment
DacharyBack on the topic of auditing code examples in our documentation, part 5 of my 8-part series dives deep into the data model we used to store metadata and the code examples.
I look at the types of queries this model enables, performance implications, and future iteration. Data modeling is one of my favorite exercises, and I'm happy with the outcome for this project!
#TechnicalWriting #DeveloperDocs
https://dacharycarey.com/2025/04/27/audit-model-code-example-metadata/
When we started talking about performing a code example audit, I wondered if we could use an LLM to help us categorize our code examples. I built a quick PoC on a Friday, and we ended up iterating on it to perform the audit.
Spoiler alert: LLM categorization accuracy plunged from 90% to 36.7% using real-world data.
This is part four in an eight-part series about auditing the code examples in our documentation.
https://dacharycarey.com/2025/03/23/audit-ai-assisted-classification/
When we built tooling to audit the code examples in our documentation, we had to decide how to access the data. We had a rough idea of what content we wanted to audit, but implementation required more than a rough idea. We also had to decide how to work with this content and what to do with the resulting data.
This is part three in an eight-part series about auditing the code examples in our documentation.
The next piece of auditing code examples in our documentation was deciding what information we actually want to track. Code example category was definitely one, but there were many ways to slice the data. What should we track?
This is part two in an eight-part series about auditing the code examples in our documentation.
Beaver Notes🚀 Beaver Notes Community Update! 🔧 Comprehensive Developer Docs! 🛠️ Setup, build, and testing insights for note-taking's future! 📝
🆕 "Embracing ARM Support" Blog: Empowering the community with ARM64! 🚀
Stay ahead! Read now!
https://www.beavernotes.com/resources/blog/pages/embracing-arm.html
🚧 Docs WIP: Improving! Not accessible on mobile yet. Stay tuned! 🚧📱
https://www.beavernotes.com/docs.html
#BeaverNotes #DeveloperDocs #ARMsupport #CommunityPower #NoteTaking #TechUpdates
