How @Mira_Network Builds a Verification Technology Moat for Learnrite

➤ Controlling Error Rates from the Source to Reshape Content Credibility

The traditional educational content production process has very high quality requirements, especially in exams like UPSC, where a mistake in one question can mislead millions of candidates. Learnrite had a 25% error rate when using AI language models to generate questions. The involvement of @Mira_Network changed this situation by employing multi-model cross-validation, ensuring that multiple models must agree on each question before approval, thereby guaranteeing content accuracy.

➤ Reducing Costs to Achieve Resource Equality

Previously, when models were not in use, some high-quality questions relied on human effort and were relatively costly. For student groups, high payments were unaffordable for many. The emergence of the @Mira_Network verification system significantly reduced the efficiency costs to a very low level, allowing expansion to more low-income groups and ensuring equitable distribution of educational resources. From my upbringing, I deeply understand the importance of this approach. When I was in school, I needed to find a complex question for analysis and had to pay high fees to hire a teacher or an online expert. Now, with the advent of AI, many problems can be resolved with just one question.

➤ Expanding the System Across Different Scenarios

The emergence of the verification system is not limited to UPSC; it can also be applied to IELTS, TOEFL, and K-12 education. This allows Learnrite to quickly replicate its successful experiences and enter other categories and national markets without having to rebuild the content review mechanism from scratch for each question bank.