diff --git a/Document/content/1.0_Introduction.md b/Document/content/1.0_Introduction.md
index 04a6cd9..9a463ee 100644
--- a/Document/content/1.0_Introduction.md
+++ b/Document/content/1.0_Introduction.md
@@ -10,7 +10,7 @@ AI testing is no longer just about security, it is a multidisciplinary disciplin
 
 ### Why AI Testing is Unique
 
-Traditional software testing focuses on protecting systems from unauthorized access, code flaws, and system vulnerabilities. 
AI systems require more. Because AI models learn, adapt, generalize, and fail in non-deterministic ways, they introduce risks that cannot be addressed with conventional security testing.
+Traditional software testing focuses on protecting systems from unauthorized access, code flaws, and system vulnerabilities. AI systems require more. Because AI models learn, adapt, generalize, and fail in non-deterministic ways, they introduce risks that cannot be addressed with conventional security testing.
 
 From the evidence documented in the NIST AML Taxonomy and the OWASP Top 10 for LLM Applications 2025 , we know that AI systems fail for reasons that go far beyond security:
 
@@ -34,8 +34,8 @@ AI models can be fooled or manipulated by carefully crafted inputs (adversarial
 ### Purpose and Scope of the OWASP AI Testing Guide
 
 The OWASP AI Testing Guide provides:
-- A standardized methodology for trustworthiness testing of AI and LLM-based systems

-- Repeatable test cases that evaluate risks across:
+* A standardized methodology for trustworthiness testing of AI and LLM-based systems

+* Repeatable test cases that evaluate risks across:
   - AI Application Layer
   - AI Model Layer
   - AI Infrastructure Layer