Scaling Documentation with AI: Dagster's Strategic Approach

Documentation Strategy

Learn how Dagster's team scales documentation and supports its open-source community using smart information architecture and AI, answering 16,000+ questions monthly.

A Dagster Labs representative previously detailed how a small team effectively supports a rapidly expanding open-source community around Dagster, a declarative data orchestration framework.

Following community feedback regarding missing and unclear documentation, the team undertook a comprehensive content audit. They subsequently redesigned Dagster’s information architecture, aligning it with the software development lifecycle. This involved rewriting documentation, migrating to Docusaurus, and structuring content for diverse user personas. The new content suite includes reference documentation, readily available code examples, real-world open-source pipelines, blog posts, an e-book, and educational courses, which have already seen tens of thousands of completions.

Artificial intelligence has significantly streamlined the team’s processes. With clear contributing guidelines within the repository and documentation co-located with code, large language models (LLMs) such as Claude Code can now generate initial drafts of tutorials from implementations, review outlines, and translate material across various mediums. Furthermore, a custom "Ask AI" assistant, powered by Dagster's documentation, GitHub issues, and discussions, currently handles over 16,000 community questions each month, providing a rapid feedback loop for identifying content gaps.

Key Takeaways

  1. Treat documentation and information architecture as core infrastructure: Well-structured, lifecycle-oriented documentation, examples, and courses benefit both human users and LLMs, enhancing the effectiveness of all educational initiatives.
  2. Build tight feedback loops with your community: Utilize platforms like GitHub, Slack, analytics, and AI assistants to identify user pain points, then continuously refine content based on real-world questions and blockers.
  3. Leverage AI and collaboration to scale a small team: Thoughtfully designed CONTRIBUTING guidelines, monorepos, and AI tools can transform engineers and community members into effective documentation contributors, with human oversight ensuring final review and maintaining the authentic voice.

Ultimately, in an AI-driven world, high-quality educational material and empathetic user engagement are more crucial than ever. AI can empower a small team and scale contributions, but its efficacy is directly tied to the underlying documentation quality and established community practices.