Trey Menefee (🇭🇰 🇹🇭 🇺🇸)

@comparativist.bsky.social

577 Followers 2259 Following

Founder of Synthetic Intelligence Group 🏝️ Escaped Florida Man | South China (20 yrs) 🇭🇰 Hong Konger | 🇹🇭 In Thailand |

The hallucinations you’re encountering occur because you’re asking the model to infer knowledge it doesn’t possess, which it compensates for by generating plausible but inaccurate outputs due to its design as a probabilistic next-token predictor, not a database. To improve prompts, understand the model’s strengths (broad generalizations, well-established facts) and weaknesses (specific datasets, niche queries) and recognize that reinforcement learning biases it toward being “over-eager to please.” For fact-checking, tools like Perplexity or models integrating chain-of-thought reasoning and web grounding are better equipped to handle such queries, while current models like ChatGPT-4o are limited by their inability to reason robustly or validate niche, underrepresented datasets.
I would push back, respectfully, on the premise that these models need to meet the deterministic standards of traditional software systems. These are fundamentally different tools, operating probabilistically, and their purpose is not to replicate the reliability of a calculator or spreadsheet. Instead, we should be focusing on informed use—identifying where these models excel and validating best practices for those tasks. For instance, LLMs like GPT-4 are remarkably effective at summarizing dense texts, performing sentiment analysis, organizing data into structured formats, and generating coherent prose for ideation. These are not areas where they just compete with humans; they often exceed human capability. But when it comes to more technical tasks—like math or running simulations—current use demands a much more cautious, evidence-based approach.
Prompt 1 The line “呪爾非爾疏” can indeed be challenging to interpret due to the classical Chinese grammar and the multiple meanings of characters used. “呪” can mean “curse,” “pray for,” or “incantation.” “爾” is a classical pronoun meaning “you.” “非” can mean “not” or “wrong.” “疏” has several meanings including “neglect,” “sparse,” “distant,” or “unfamiliar.” The literal translation could be “Curse you, not you neglect” which can be understood as “Cursing you is not your neglect.” This doesn’t sound very grammatical in English and may not capture the intended meaning. The translation as “I wouldn’t slight you” might be an attempt to interpret “非爾疏” as “it’s not that you’re being neglected/slighted.” However, the structure is still quite unusual, and the presence of “呪,” meaning “curse” or “pray for,” complicates the interpretation. If we consider “呪” as “pray for,” a possible interpretation might be “Praying for you, it’s not neglect.” But again, this is speculative and the exact meanin