diff --git a/docs/tech_docs/llm/ai_over_view.md b/docs/tech_docs/llm/ai_over_view.md
index 84f615a..4d8d245 100644
--- a/docs/tech_docs/llm/ai_over_view.md
+++ b/docs/tech_docs/llm/ai_over_view.md
@@ -175,4 +175,90 @@ Explainable AI focuses on developing techniques to make AI models more transpare
 - Causal Inference for Explanations
 - Interpretable Deep Learning
 - Adversarial Attacks on Explanations
-- Human-Centered Explainable AI
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+- Human-Centered Explainable AI
+
+---
+
+Certainly! Here's a framework for each distinct type of document you'll likely create as you build out your AI fundamentals documentation:
+
+1. Overview Document:
+   - Purpose: Provide a high-level overview of the entire AI fundamentals documentation.
+   - Outline:
+     - Introduction to AI and its importance
+     - Brief description of each module and its focus
+     - Target audience and prerequisites
+     - Learning objectives and outcomes
+     - Navigation guide and how to use the documentation effectively
+
+2. Module Introduction Document (for each module):
+   - Purpose: Introduce the specific AI topic covered in the module and set the context.
+   - Outline:
+     - Definition and scope of the AI topic
+     - Importance and relevance of the topic
+     - Key concepts and terminology
+     - Real-world applications and impact
+     - Prerequisites and recommended background knowledge
+     - Learning objectives and outcomes for the module
+
+3. Concept Explanation Document (for each subtopic within a module):
+   - Purpose: Provide a detailed explanation of a specific concept, technique, or algorithm.
+   - Outline:
+     - Introduction and definition of the concept
+     - Theoretical background and underlying principles
+     - Mathematical formulations or algorithmic steps (if applicable)
+     - Illustrative examples or visualizations
+     - Advantages, limitations, and trade-offs
+     - Practical considerations and implementation details
+     - Code snippets or pseudocode (if applicable)
+     - Related concepts and references for further reading
+
+4. Tutorial or Walkthrough Document (for hands-on exercises or projects):
+   - Purpose: Guide readers through a step-by-step practical implementation of a specific technique or project.
+   - Outline:
+     - Introduction and objectives of the tutorial
+     - Prerequisites and setup instructions
+     - Step-by-step guide with code explanations
+     - Data preparation and preprocessing
+     - Model training and evaluation
+     - Results interpretation and analysis
+     - Variations and extensions
+     - Troubleshooting and common pitfalls
+     - Conclusion and further exploration
+
+5. Case Study Document (for real-world applications and success stories):
+   - Purpose: Showcase real-world examples and success stories of AI applications in various domains.
+   - Outline:
+     - Introduction and background of the case study
+     - Problem statement and challenges faced
+     - AI techniques and approaches applied
+     - Data sources and preprocessing steps
+     - Model architecture and training process
+     - Evaluation metrics and results achieved
+     - Lessons learned and best practices
+     - Impact and benefits of the AI solution
+     - Future prospects and scalability
+
+6. Resource Collection Document (for each module or topic):
+   - Purpose: Curate a list of valuable resources, references, and learning materials.
+   - Outline:
+     - Books and research papers
+     - Online courses and tutorials
+     - Videos and webinars
+     - Blogs and articles
+     - Open-source libraries and tools
+     - Datasets and benchmarks
+     - Community forums and discussion groups
+     - Conferences and workshops
+
+7. Glossary Document:
+   - Purpose: Define and explain key terms, acronyms, and concepts used throughout the documentation.
+   - Outline:
+     - Alphabetical listing of terms
+     - Clear and concise definitions
+     - Cross-references to related terms
+     - Examples or illustrations (if applicable)
+     - Acronym expansions and abbreviations
+
+These distinct document types serve different purposes and cater to various aspects of learning and understanding AI fundamentals. They range from high-level overviews to detailed concept explanations, practical tutorials, real-world case studies, curated resources, and a comprehensive glossary.
+
+By creating these different types of documents, you can provide a holistic and multi-faceted learning experience for your readers. They can choose the documents that align with their learning style, goals, and level of expertise, allowing for a personalized and effective learning journey.
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