Unverified50% confidenceOpinionExact time
The limitations of semanticfa include: the choice of embedding model affects the results, the handling of reverse-scored items is not yet ideal, and factor retention decisions may depend on additional rules.
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50%
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Long-term
Relevance
10/28/2026
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UnverifiedDecisionAct's model selection can still make mistakes, and users need to reserve fallback logic; the clarity of parameter descriptions directly determines input parsing accuracy, and in practice descriptions should be written as distinguishable discriminating features as much as possible, with a failure fallback set for each type of candidate.79% similarUnverifiedRelying on prompt constraints to prevent dangerous operations is inherently unreliable, because the model may be jailbroken, make misjudgments, or forget constraints during long conversations.75% similarUnverifiedWhen a model gives answers of vastly different quality for semantically equivalent but differently phrased questions, it indicates that the model has overfitted to surface-level wording rather than truly mastering the underlying task.75% similarUnverifiedWhen early model capabilities were insufficient, large amounts of guiding prompts were needed, but those prompts in turn constrained the boundaries of the model's capabilities.74% similarUnverifiedHaving a large model both make decisions and provide reasoning carries a risk: the reasoning the model provides may be a post-hoc rationalization of an already generated conclusion rather than the true basis for the decision.73% similar
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