BaZi Blog
4,948 articles on BaZi and the neighbouring arts, shelved by topic. Search the titles, or browse by shelf.
- Manually Plot Your BaZi Once: A Reference for VerificationThis page guides you through manually plotting a complete BaZi chart, covering the steps for deriving the year, month, day, and hour pillars.
- AI Face Reading and BaZi AnalysisThis page introduces the AI face reading service, covering both physiognomy/palm reading and BaZi analysis.
- AI Liu Yao: Dynamic Hexagram Analysis Based on CastingThis page introduces the basic principles of AI Liu Yao. Liu Yao requires a live hexagram casting action, obtaining the hexagram through coin tossing or number selection, then analyzing it with time and line positions. Unlike BaZi, which is derived directly from birth data, Liu Yao emphasizes the state at the moment of casting, so the same question cast at different times may yield different hexagrams.
- AI in Chinese Metaphysics: Digitizing Traditional SystemsThis page explains how AI is used to digitize and organize the rule systems of traditional Chinese metaphysical arts such as Bazi, Zi Wei Dou Shu, Liu Yao, Qi Men Dun Jia, and date selection. It focuses on technical implementation and knowledge structuring, including chart generation, rule retrieval, and case analysis, without making any predictions or promises about personal destiny.
- Hidden Stems Mislabeling: Fixed Correspondence of Earthly BranchesThis page explains the fixed correspondence between earthly branches and hidden stems, and points out common mislabeling scenarios.
- Common AI Paipan Errors: Lichun and Year Pillar BoundaryThis page summarizes common errors in AI-generated BaZi charts, focusing on mishandling of the Lichun boundary and year pillar transition rules.
- AI BaZi Solar Term Errors: Month Pillar Changes at JieThis page explains common AI BaZi charting errors regarding solar terms. , Yushui, Chunfen).
- AI Bazi Charts: Li Chun Boundary ErrorsThis page explains common AI bazi chart errors in year pillar calculation: the year pillar changes at Li Chun (Start of Spring), not on January 1 or Lunar New Year. Those born before Li Chun belong to the previous year's pillar. Understanding this helps spot potential inaccuracies in chart output, without implying any guarantee of predictive accuracy.
- Mixing Lunar and Solar Dates in Chart CalculationThis page explains the common error of entering a lunar calendar date as a solar date when generating a chart.
- Time Zone and Daylight Saving Time: Confirming Birth TimeThis page explains how the clock time on a birth certificate corresponds to time zone and daylight saving time rules, and why confirming this information is necessary for chart construction. The birth certificate records the local clock time at the moment of birth, but the time zone and whether daylight saving time was in effect must be confirmed separately. This page provides confirmation methods and common considerations, without making any predictions or promises.
- AI Chart Calculation: Rule-Based Conversion to Four PillarsThis page explains the fundamental mechanism of AI chart calculation: converting Gregorian date and time into four pairs of sexagenary cycle pillars for year, month, day, and hour according to established calendrical rules. The process is deterministic, with each step defined by clear rules; identical inputs always produce identical outputs. The content is intended for users seeking to understand the calculation logic, without any predictive claims.
- True Solar Time: Correction by Longitude and Equation of TimeThis page explains the concept of true solar time and its correction procedure. It covers how to convert standard clock time to local solar time based on the longitude of the birthplace, and explains the role of the equation of time. This conversion allows for more accurate determination of the birth hour, which is used for time calibration in Chinese astrology charts.
- Vague AI Fortune-Telling: No Chart Elements ReferencedThis page examines a key trait of vague AI fortune-telling: descriptions that never reference any BaZi chart elements such as Heavenly Stems, Earthly Branches, or Ten Gods. It shows how such generic statements lack specificity and differ from chart-based readings. The goal is to help readers recognize vague language, without making any claims about predictive accuracy.
- AI Fortune Telling Accuracy: How LLMs Generate BaZi ReadingsThis page examines how large language models like Grok are used in BaZi (Chinese astrology) readings, covering the generation of Four Pillars charts and interpretive text. It focuses on the underlying mechanics, typical output patterns, and factors affecting consistency. Readers can gain a clear understanding of the current capabilities and limitations of AI-based fortune telling.
- AI Fortune-Telling and Fabricated CitationsThis page explains how AI fortune-telling produces fabricated citations. Language models generate text sequences based on linguistic patterns, so book titles, volumes, and clauses fall within the model's probable outputs, appearing plausible but not sourced from reliable documents. Understanding this helps identify pseudo-citations in AI-generated content.
- Is AI Fortune Telling Reliable? A Four-Layer BreakdownThis page breaks down AI fortune telling into four layers: input, chart calculation, terminology, and conclusion.
- Is AI Fortune Telling Reliable?This article analyzes AI fortune telling from two dimensions: accuracy and reliability.
- Can You Trust AI Fortune-Telling? A Practical GuideThis page examines the credibility of AI fortune-telling, focusing not on accuracy rates but on how to rationally view generated content.
- Contradictions in AI Readings: Three Forms and CausesThis page categorizes common forms of contradictions in AI-generated long-form readings, including conflicting claims, logical breaks, and inconsistent details, and examines the underlying generation mechanisms. It aims to help users understand why such contradictions occur, without making any promises about predictive accuracy.
- Will AI Replace Chart-Casting Tools?This page examines the functional differences between chart-casting tools and language models.
- AI Misusing Fate-Calculation Terms: Daily Meaning InterferenceThis page explains how fate-calculation terms that share the same written form as everyday Chinese words, such as 'harm', 'robbery', 'kill', and 'seal', are prone to being misused by AI models based on their common meanings. It focuses on the manifestations and causes of such errors, helping users identify and avoid misuse of professional terminology. No predictive or promissory statements are involved.
- AI Fortune-Telling Number Errors: Why Models Struggle with ArithmeticThis page explains why language models often make errors when generating numbers.
- AI Fortune Telling Recommendations: Product Types and GuideThis page introduces the common types of AI fortune-telling products, including general conversational models, websites that combine astrological charting tools, and standalone numerology apps. It covers the features, usage, and typical scenarios of each type, helping users understand the differences and make informed choices based on their needs.
- AI Agreeing with You: Recognizing Sycophantic ResponsesThis page explains that when users include a conclusion in their prompts, AI models often organize responses around that conclusion instead of making independent judgments. It describes how this tendency manifests, helping users understand potential bias in answers and encouraging more careful wording of questions to obtain more objective responses.
- Is AI Fortune-Telling Useful? Three Layers of EvaluationThis page evaluates the usefulness of AI fortune-telling on three levels: time-saving, clarifying terminology, and providing actionable judgments.
- AI Fortune-Telling Overreach: Five Question Types to AvoidThis page identifies five categories of questions that AI fortune-telling services should not answer: medical diagnosis, legal and litigation judgments, investment and lending decisions, lifespan and life-and-death matters, and judgments involving third-party privacy. These boundaries help prevent inappropriate guidance and keep the service within a reasonable scope of reference. Users should seek advice from qualified professionals on such matters.
- AI Ignoring the Month Branch: Role and Limits in Bazi AnalysisThis page explains the conventional role of the month branch in Bazi analysis and why an AI model may ignore it.
- AI Fortune Telling Popularity in Hong KongThis page introduces the search popularity and market overview of AI fortune telling in Hong Kong.
- AI Fortune-Telling Accuracy: How to Interpret and EvaluateThis page discusses the meaning and evaluation of accuracy in AI fortune-telling.
- Self-Created Shen Sha: Markers Derived from Fixed RulesThis page explains the concept of self-created Shen Sha, describing them as markers derived from a natal chart using fixed rules.