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How We Create Content for the Learning Center

How the Scalr team produces Learning Center content, from topic research and user interviews to fact-checking, neutrality review, and editing.

1. Topic Research

We start by picking topics that match what users need. The team finds candidates by looking at:

  • Questions asked on subreddits like /r/terraform or /r/devops.
  • Problems and questions from the Scalr community.
  • Industry developments and challenges, especially in platform engineering.

2. User Interviews

Scalr users are often involved throughout the content creation process. Talking to them checks whether the problems we've identified are real and whether the proposed approaches and solutions hold up. Those conversations push us to examine an issue more thoroughly, keep the content from staying superficial, and sometimes expose our own blind spots.

3. Fact Checking

We verify every significant piece of information, including code samples, technical details, and examples. Accuracy comes first. Verification means:

  • Consulting original sources.
  • Referencing reputable studies or organizations.
  • Obtaining insights from subject matter experts.

4. Neutrality Review

Each piece gets a neutrality review so the information is presented fairly and completely. The reviewer looks for:

  • Language that could unduly influence opinion.
  • One-sided presentations where multiple perspectives exist.
  • Unstated assumptions.

5. Spellcheck & Grammar

We run standard spelling and grammar checks. A human editor then reviews the text for errors automated tools miss, so the final copy reads clearly.

6. AI for Clarity and Readability

We use two AI tools, Google's Gemini and Anthropic's Claude, at one specific stage of the workflow. That stage comes after the human team has finished the core work: research, writing, fact-checking, and neutrality review.

The AI's job is to help with clarity and readability, for example by:

  • Suggesting revisions for complex sentences.
  • Proposing simpler alternatives for technical jargon.
  • Identifying areas where narrative flow can be improved.

Human editors review every AI suggestion and decide whether to use it. The AI refines the text but doesn't make final editorial decisions; the human team keeps full control over the content.

About the author

Sebastian Stadil

CEO at Scalr

Sebastian Stadil is the CEO of Scalr with 15+ years of DevOps experience. He started with AWS in 2004 and advised early Microsoft Azure and Google Cloud.