AI Tools for Chinese Metaphysics Practitioners

The use of AI by a 命理师 is not itself a problem. Chart calculation has long depended on software, and using tools for language organization is also a common practice. What matters is not whether a tool was used, but where in the process it was used and whether the practitioner can distinguish tool-assisted drafting from their own judgment.

这件事本身不是问题

A 命理师 who uses tools to assist in writing is not automatically doing anything improper. The practice of casting a chart already relies on computational engines; manual calculation is rare and error-prone. Similarly, using software or language models to help organize written material is, in itself, a normal part of modern working methods. The question should not be framed as a moral binary between using and not using AI. It should be framed as a question about where the tool stops and the practitioner begins.

In many fields, AI-assisted drafting is now routine. The same applies here. What would be misleading is not the presence of a tool, but the absence of disclosure when the tool has taken over the part of the work that requires human judgment. The use of AI for sentence structure, formatting, or term explanation does not remove the practitioner from the process. The use of AI for the final interpretive judgment would.

要问清楚的是哪一段用了

The practical question to ask is not whether AI was used, but which stage of the report involved AI. A useful way to divide the process is into four parts: 排盘，术语解释，结论组织, and 最终判断.

The first three stages can reasonably involve tools. 排盘 is already handled by calculation software. 术语解释 can be generated by a language model without difficulty, because it is definitional rather than interpretive. 结论组织, the arrangement and phrasing of conclusions, can also be assisted by a model, provided the conclusions themselves come from the practitioner.

The final stage is different. If 最终判断 is generated by a model, then no one is actually making the judgment. The report may look complete, but the role of the practitioner has been replaced at the exact point where expertise is supposed to operate. A reader who understands this distinction can ask a more precise question than simply whether AI was used.

三个可以观察的迹象

There are three observable signs that a report may have been generated or heavily assisted by a language model in ways that affect its reliability.

The first sign is the appearance of 干支 characters that are not present on the chart. A model may generate plausible-sounding references to stems and branches that do not actually appear in the user's 命盘. A practitioner working from the chart would not make this error, because the chart is fixed and visible.

The second sign is inconsistency in terminology. If the same report defines the same term in two different ways, that is a sign that sections were generated separately without a single controlling understanding. A human writer may vary phrasing, but the underlying definition should remain stable.

The third sign is the most common: a long text with very little specific detail. Language models tend to expand one point into multiple paragraphs. Human writers, especially experienced practitioners, tend to compress multiple points into one paragraph. A report that feels fluent but contains few concrete references to the chart, few dated events, and few specific 干支 interactions may be the product of expansion rather than analysis.

直接问出来的一句话

There is one direct question that can be asked: 这份报告里哪些部分是您自己判断的. This question is simple and does not require the client to understand technical details. It asks the practitioner to identify the boundary between their own judgment and any tool-assisted material.

If the practitioner can answer this question clearly, that itself is informative. Being able to answer means the practitioner knows where the boundary is. If the answer is vague, defensive, or avoids the question, that is also informative. The question does not require the practitioner to reject AI. It requires them to show that they can distinguish their own interpretive work from generated text.

This question is more useful than asking whether AI was used, because a yes or no answer to the latter tells the client nothing about what the AI did. The former question asks for a description of responsibility, which is harder to fake and easier to verify.

披露与不披露的差别

The difference between disclosure and non-disclosure is not about whether AI was used. It is about whether the reader can adjust their level of scrutiny accordingly.

If a report discloses that the chart was calculated by an engine and the interpretive text was written by a human, the reader knows to verify the chart against another source and to focus their attention on the interpretation. If a report discloses that the text was generated by a model, the reader knows to check the specific 干支 references, the consistency of terminology, and the presence of concrete detail. Disclosure does not make a report worthless; it makes verification possible.

Non-disclosure removes that possibility. The reader cannot tell which parts of the report came from judgment and which parts came from generation. The entire report becomes harder to evaluate, not because any single part is necessarily wrong, but because the reader does not know where to look.

The practical difference is therefore one of epistemic responsibility. Disclosure allows the reader to calibrate. Non-disclosure forces the reader to treat everything as suspect, which is more work and less trust.

本站的做法

This site states its own practice plainly. The chart calculation is done by an engine. The written interpretation is generated by a model. The site also states which parts do not involve the model.

This is a form of disclosure that distinguishes three layers: the computational layer, the generative layer, and the layer where no model is used. It does not claim that the generated text is equivalent to human judgment. It does not hide the use of a model. It tells the reader what they are receiving.

The question of whether this site's practice is acceptable is separate from the question of whether it is transparent. Transparency is the minimum condition for evaluation. A reader who knows the method can decide whether to use the result. A reader who does not know the method cannot make that decision.

把这件事问清楚的成本

The cost of asking the question is low. It is one question, one sentence, asked before payment. It does not require technical knowledge or a long investigation. It takes less than a minute.

The value of the answer is high. It reveals whether the practitioner has thought about the boundary between their own judgment and generated text. It reveals whether the practitioner is willing to be specific about method. It reveals whether the report is likely to contain the three observable signs described above.

A practitioner who can answer the question directly is more likely to produce a report that can be checked. A practitioner who cannot or will not answer is more likely to produce a report that cannot be checked. The question is worth asking before paying, because the cost of asking is trivial and the cost of not asking is an unverifiable report.