This approach requires sourcing and maintaining accurate information, which means you can't fabricate numbers or exaggerate metrics. AI models increasingly cross-reference claims across sources, and inconsistencies damage credibility. The data you include must be truthful and, where relevant, attributed to primary sources. But when you consistently provide specific, accurate information, you build a reputation as a reliable source that AI models return to repeatedly.
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Because every interaction passes through runEffect, we can easily implement a redaction layer to scrub personally identifiable information, like credit card numbers or emails, before they ever hit the trace log.