What is AI Hallucination?

AI Hallucination refers to content generated by AI models that contains false, inaccurate, or unverifiable information. These pieces of information may sound plausible but are actually incorrect. As a professional AI Hallucination Fact-Checker, AgentVerify Blade extracts key facts such as numerical values, dates, entities, and strong claims from text through Structured claim extraction logic, helping users identify potential AI hallucinations.

How to Use the Fact-Checking Template (Zero-knowledge Verification Report)?

As a Local Markdown report generator for AI agents, AgentVerify Blade automatically generates structured fact-checking report templates (Zero-knowledge verification report) containing serial numbers, extracted facts, type classifications, verification status, and evidence source placeholders. Users simply input AI text into the system to obtain standardized verification templates, then verify each factual claim one by one and fill in evidence sources.

What Types of Structured Claim Extraction Does AgentVerify Blade Support?

AgentVerify Blade supports four core fact extraction types (Structured claim extraction logic): 1) Numerical extraction - identifies percentages, currencies, and large numbers; 2) Temporal extraction - identifies specific dates, years, and time periods; 3) Entity extraction - identifies company names, personal names, legal provisions, and geographic locations; 4) Strong claim identification - identifies absolute statements like "first", "only", "always", "most...", etc. As an AI Hallucination Fact-Checker, these types are all high-risk areas for AI hallucinations.

Do I Need an API Key to Use AgentVerify Blade?

No. AgentVerify Blade is a fully locally-run tool (Local Markdown report generator). All processing is done in the browser without relying on any external API or network services. Your data is not uploaded to any server, ensuring privacy and security. As a Zero-knowledge verification report tool, your original text and verification results never leave your device.

How to Share Fact Verification Reports?

AgentVerify Blade offers multiple sharing methods: 1) Click the "Copy Markdown Report" button to get a standard Markdown format Zero-knowledge verification report; 2) The system automatically encodes analysis results into the URL Hash, allowing you to share the current page link directly (Local Markdown report generator for AI agents mode). Recipients can see the same verification results when they open the link.

AgentVerify Blade - Local AI Hallucination Fact-Checker & Markdown Report Generator

AI Text Input (AI Hallucination Source)

Quick Load Cases (Case Library for AI Hallucination Testing)

Fact-Check Report Preview (Zero-knowledge Verification Report)

Technical Guide: How to Identify and Verify AI Hallucinations

AgentVerify Blade is a professional AI Hallucination Fact-Checker specifically designed to detect and verify false information in AI-generated content. As a Zero-knowledge verification report tool, this application systematically extracts and validates factual claims from Large Language Model (LLM) outputs through locally-run Structured claim extraction logic. This tool also serves as a Local Markdown report generator for AI agents, helping users generate structured fact verification reports.

AI Hallucination refers to content generated by large language models that contains false, inaccurate, or unverifiable information. AgentVerify Blade helps you systematically identify and verify AI-generated content through a structured fact extraction and verification process.

Fact Extraction Engine Working Principle (Structured Claim Extraction Logic)

Numerical Extraction: Identifies percentages (e.g., "increased by 30%"), monetary amounts (e.g., "$50 billion"), large numbers, and other quantifiable claims

Temporal Extraction: Captures specific dates, years, time periods, and other time-related claims (e.g., "2024", "past five years")

Entity Extraction: Identifies named entities such as organizations (e.g., "IBM"), personal names, geographic locations, and legal provisions

Strong Claim Identification: Detects absolute statements (e.g., "first", "only", "always", "most...") which are often high-risk areas for AI hallucinations

Verification Best Practices

Prioritize verification of factual claims containing specific numerical values and dates

Maintain high vigilance for absolute statements like "first", "only", etc.

Cross-verify multiple authoritative sources to avoid relying on single sources

Systematically document the verification process using the generated Markdown report template

Privacy & Security (Zero-Knowledge)

AgentVerify Blade is a fully local tool. All text processing is completed in your browser without uploading any data to external servers. Your sensitive information and verification content remain local at all times, ensuring data privacy and security. As a Zero-knowledge verification tool, your original text and verification results never leave your device.

Extraction Methodology

AgentVerify Blade employs regex-based Structured claim extraction logic to systematically identify key information in AI-generated content. As a professional AI Hallucination Fact-Checker, this tool implements a complete local verification process that protects user privacy throughout — a true Zero-knowledge verification report approach.

Numerical Extraction Logic

The numerical extraction module uses multi-pattern regex to identify quantitative claims in text, including:

Percentages: Matches percentage expressions like "30%", "15.5%"
Monetary amounts: Identifies amounts with currency symbols (¥100, $1,000, €50) and Chinese currency units (500亿, 100万, 1000元)
Large numbers: Captures pure numbers with 4+ digits, typically representing quantities, scales, or statistics
Regex Pattern: /(\d+(?:\.\d+)?\s*%|\d+(?:\.\d+)?\s*(?:亿|万|千|元|美元|欧元)|[¥$€]\s*\d+(?:,\d{3})*(?:\.\d+)?|\d{4,}(?:,\d{3})*(?:\.\d+)?)/g

Numerical claims are high-risk areas for AI hallucinations, as large language models often fabricate specific statistics or exaggerate numbers.

Temporal Extraction Logic

The temporal extraction module captures time-related claims in text, including:

Specific dates: Identifies Chinese date formats (2024年, 2024年1月1日) and ISO formats (2024-01-01)
Year references: Extracts standalone year claims, commonly used for historical events or future predictions
Time periods: Captures relative time expressions (近五年, 过去几个月, 未来几年)
Regex Pattern: /(\d{4}年(?:\d{1,2}月(?:\d{1,2}日)?)?|\d{4}-\d{2}-\d{2}|\d{2}\/\d{2}\/\d{4}|(?:近|过去|未来|去年|今年|明年)\s*(?:几)?年|(?:近|过去|未来)\s*(?:几)?个月)/g

Time claims require special attention, as AI may incorrectly associate events with times or fabricate non-existent time points.

Entity Extraction Logic

The entity extraction module identifies named entities in text, including:

Organizations: Matches company, group, university, and research institute names (IBM公司, 清华大学, 中国科学院)
Geographic locations: Identifies countries, cities, provinces, and natural geographic names (北京市, 长江, 珠穆朗玛峰)
Legal provisions: Extracts names of laws, regulations, and rules (数据安全法, 民法典)
Personal names: Identifies English name formats (John Smith, Mary Johnson)
Regex Pattern: /([\u4e00-\u9fa5]{2,}(?:集团|公司|股份|科技|有限|大学|研究院|政府|部门|机构|组织|协会|基金会)|[\u4e00-\u9fa5]{2,}(?:市|省|区|县|镇|村|山|河|湖|海)|[\u4e00-\u9fa5]{3,}(?:法|条例|规定|办法)|[A-Z][a-z]*\s+[A-Z][a-z]*(?:\s+[A-Z][a-z]*)*)/g

Entity claims are central to fact-checking, as AI frequently fabricates non-existent organizations, locations, or incorrectly cites legal provisions.

Strong Claim Identification Logic

The strong claim identification module detects absolute statements in text, which are often high-risk areas for AI hallucinations:

Exclusive claims: Identifies exclusive terms like "第一", "唯一", "仅", "只有"
Absolute claims: Captures absolute expressions like "总是", "从不", "绝对", "完全"
Superlative claims: Extracts superlative terms like "最...", "顶级", "领先", "权威"
Necessity claims: Identifies mandatory expressions like "必须", "一定", "肯定", "所有", "任何"
Regex Pattern: /(第一|唯一|总是|从不|最[高快大强好优]|领先|顶级|权威|绝对|完全|全部|所有|任何|必须|一定|肯定|毋庸置疑|无可争辩|不容置疑)/g

Strong claims require the highest level of verification, as AI tends to use absolute language to enhance persuasion, but these claims often lack factual basis.

Deduplication & Classification Logic

The deduplication and classification module ensures accuracy and structure of extraction results:

Composite key deduplication: Uses "content + type" as unique identifier to avoid extracting duplicate factual claims
Set data structure: Leverages JavaScript Set's efficient lookup for O(1) time complexity deduplication
Type classification: Each extracted fact is automatically categorized into numerical, temporal, entity, or strong claim categories
Status marking: All extracted facts are marked as "UNVERIFIED" by default, awaiting manual verification
Source placeholder: Generates "[Source: Click to add source link]" placeholder for each fact to guide users in adding evidence

Deduplication logic ensures report conciseness and prevents redundant information from interfering with the verification process.

Report Generation Logic

The report generation module transforms extracted facts into structured Markdown format:

Timestamp: Automatically generates current date as report generation time
Table structure: Uses Markdown table format to display serial number, extracted fact, type, verification status, and evidence source
HTML rendering: Renders Markdown table as HTML table in preview area for easy viewing
Status highlighting: Unverified status displayed in red text to alert users to pending verification
Export functionality: Supports exporting reports as standard Markdown source for archiving and sharing

Structured report templates ensure systematic and traceable verification processes.