This doesn't mean stuffing your content with random numbers. It means grounding your claims in specific, verifiable data wherever possible. Instead of writing "Our tool is widely used," you'd write "Our tool has 150,000 monthly active users with a 4.7 out of 5 satisfaction rating based on 3,200 reviews." The specificity signals credibility to AI models, which learned during training that precise data indicates reliable sources.
'They are essential': How smoke detectors are evolving
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I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.