Off-the-shelf LLMs can sound confident about financial decisions while changing a recommendation after small input changes. In anonymized business examples, Udi Menkes argues that generic growth advice can miss cash-flow pressure, customer concentration or a vendor's role in generating revenue.
The proposed method derives business states, actions and later outcomes from comparable records. It uses causal comparisons to distinguish correlation from the effect of a decision, lets a general model propose possible actions, and applies an outcome-trained model to assess them. The central distinction is between adding more context and learning from observed results.
The broader lesson is to evaluate AI recommendations against outcomes that can be checked in a given domain and to present them with enough context for people to understand the reasoning and make their own decisions.
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