CiteAura Editorial Guidelines & AI Transparency Policy

1. Core Editorial Principles

CiteAura was founded on a simple observation: Generative Engine Optimization (GEO) has been flooded with speculative myths, unverified keyword tricks, and black-box promises. Our mission is to restore empirical rigor to the discipline.

• Evidence Over Assertion: We never publish technical recommendations without reproducible empirical backing. If a guideline states that llms.txt does not impact Google Search rankings, it is because we cross-reference Google Search Central specifications and our own crawling tests.
• First-Hand Experience: Every technical guide, review, and benchmark published on CiteAura is written or peer-reviewed by active search systems engineers, distributed systems developers, or technical SEO practitioners.
• Zero Unchecked Automation: We firmly reject automated content farms and scraped summaries. Content must solve an authentic practitioner challenge.

2. Transparent AI & Automation Disclosure

We believe automation should be applied where machines excel (collecting large-scale HTTP headers, calculating statistical sampling variance, parsing JSON-LD trees) and human cognition where critical thinking is essential:

1. Where We Use Automation:
   • Running reproducible prompt cohorts across configured LLM APIs (OpenAI, Anthropic, Perplexity, DeepSeek).
   • Simulating HTTP User-Agent interactions (GPTBot, ClaudeBot, Googlebot) to check server response codes.
   • Extracting DOM trees and identifying unsemantic markup or missing entity anchors.
2. Where We Strictly Prohibit Unsupervised AI:
   • Drafting conclusions or opinions without engineer oversight.
   • Fabricating synthetic benchmark statistics without running actual reproducible runs.
   • Rewriting third-party articles for the sole purpose of keyword targeting.

3. Fact-Checking, Primary Sources, and Citations

Every guide published under CiteAura Guides must link directly to primary documentation:

• Official Search Documentation: Google Search Central documentation, OpenAI Bot documentation, Anthropic system documentation, and Microsoft Webmaster guidelines.
• Formal Web Standards: W3C recommendations, IETF RFC specifications (e.g., Robots Exclusion Protocol RFC 9309), and Schema.org entity vocabularies.
• Code Reproducibility: When code snippets or configurations are provided (e.g., Nginx robots rules or JSON-LD snippets), they are tested for syntax validity and rendering compliance.

4. Objectivity in Platform Comparisons

Our platform comparison articles (e.g., CiteAura Comparisons) adhere to the following neutrality standards:

• Public & Empirical Data: Competitor features are evaluated via active software trials, published public documentation, and empirical query tests.
• Balanced Recommendations: We recognize that different teams have different requirements. When a competing architecture is better suited for an enterprise PR listening use case, we explicitly say so.
• No Paid Placement: No vendor pays for placement, rating, or inclusion in our comparison matrices.

5. Corrections and Feedback Loop

Because search engine algorithms and LLM inference behaviors evolve rapidly, we maintain an active correction and revision log across all documentation.

If you identify a factual inaccuracy, broken spec reference, or outdated guideline in any CiteAura publication, please submit a correction notice to our editorial desk:

Email: [email protected]

Our engineering team investigates every technical inquiry and updates the affected document with a clear timestamped changelog within 48 business hours.