In an era where AI can generate any answer, factual accuracy is the ultimate asset. I do not simply “Google,” nor do I rely on random chatbot responses. I have developed a proprietary data verification protocol based on academic fact-checking principles and the hierarchy of evidence.
Here is how my fact-selection system works using a custom-configured AI assistant:
1. Initiation and Risk Assessment My process begins with questions, not search. I have designed an algorithm that mandates context definition before work begins. We always assess the risk level (YMYL — Your Money Your Life). If the topic concerns finance, health, or business strategy, a “High Reliability Protocol” is automatically activated. This eliminates superficial research.
2. Strict Hierarchy of Evidence I have trained the system to ignore information noise. We operate according to a strict source classification:
Priority (Level 1-2): only systematic reviews, meta-analyses, and large-scale Randomized Controlled Trials (RCTs). This is the foundation of credibility.
Context (Level 3): cohort studies to understand correlations.
Filter: we intentionally discard expert opinions unsupported by data and small-sample studies to avoid survivorship bias.
3. Multi-Stage Verification (CRAAP/SIFT) Finding a fact is not the finale; it is the beginning of verification. My assistant acts as a scientific peer reviewer:
It checks the methodology for “p-hacking” (data dredging).
It analyzes conflicts of interest and funding sources.
It applies lateral reading (the SIFT method) to verify the authority of the journal or author.
This approach ensures that my reports and strategies contain no “hallucinations” or marketing myths—only verified data.
Author: Yautseyeva Natallia
"With 11 years in growth and SEO, having worked with top agencies and accelerators, I am now based in Shanghai. I build a bridge between traditional SEO and emerging tech. I consult founders on how to secure their traffic as users shift to ChatGPT."
