How We Create Content
Our research process, editorial standards, and commitment to accuracy.
Every guide on LLM Guides goes through a structured process designed to produce content that's accurate, practical, and genuinely useful. Here's how we work.
Research Process
We don't write from assumptions. Every article starts with research, and we prioritize sources in this order:
OpenAI, Anthropic, Google, and other model providers publish detailed documentation. This is always our starting point.
Academic papers, technical reports, and official benchmarks provide the foundation for capability claims.
We run the prompts ourselves. When we say something works, it's because we've tested it — not because someone else said so.
We avoid relying on secondary sources, forums, or unverified claims. If we can't verify something, we don't publish it.
We Test What We Write
This is what sets our guides apart.
When we publish prompt examples, workflow guides, or model comparisons, we've actually tested them. We use ChatGPT, Claude, Gemini, and other models regularly. We know what works in practice, not just in theory.
If a technique sounds good but doesn't hold up in real use, we either fix it or don't include it. Our goal is to save you time, not waste it with advice that doesn't work.
Community Contributions
We also welcome hands-on testing from the community. If you've run documented experiments with LLMs and want to share your findings, we're interested.
Community contributions go through the same editorial review as our own work. Credit is given where it's due.
Get in touch if you'd like to contribute.
Editorial Standards
Every piece of content we publish follows these principles:
We fact-check claims against official sources. Capabilities, limitations, pricing, and technical details are verified before publication.
We don't oversell what these tools can do. Every model has weaknesses, and we cover them. You'll know what works and what doesn't.
We focus on what you can actually use. Abstract explanations are only included when they help you apply the knowledge.
We avoid buzzwords, exaggerated claims, and marketing language. If something is useful, we explain why. If it's not, we say so.
Keeping Content Current
AI models change rapidly. Features get added, capabilities improve, pricing changes, and sometimes things break.
We review and update our content regularly to ensure it stays accurate. When models receive significant updates, we revisit affected articles and revise them as needed.
Every article displays a "last updated" date so you know how recent the information is.
Corrections
We make mistakes. When we do, we fix them.
If you spot an error in any of our guides — outdated information, incorrect claims, broken examples — we want to know. Corrections are made promptly and transparently.
You can reach us through our contact page to report issues or suggest improvements.
The Bottom Line
We create the guides we wish existed when we started learning these tools — clear, tested, honest, and actually useful.
That's the standard we hold ourselves to.