Day 5 marked a strategic shift from pure development to architectural planning. Rather than diving straight into coding the RAG system for my DIIO preparation, I took time to understand how to properly document and showcase my academic work through a structured course system that mirrors the successful blog implementation.
Deconstructing the Blog Architecture
The existing blog system provides an excellent foundation for academic content organization. After studying the structure, I identified the key components that make it work:
File Organization Pattern
app/blog/
├── page.tsx # Index page listing all posts
├── utils.ts # Content reading and parsing utilities
├── [slug]/ # Dynamic routing for individual posts
│ └── page.tsx # Post rendering with metadata
└── posts/ # MDX content files with frontmatterThis clean separation of concerns between routing, content management, and presentation creates a scalable system that can be replicated for course documentation.
Content Management Strategy
The blog system's strength lies in its frontmatter-driven metadata approach:
---
title: "Post Title"
publishedAt: "2025-10-07"
summary: "Brief description"
---This pattern allows for rich metadata without complex database requirements, making it perfect for academic content that needs additional context like course codes, credits, and technology stacks.
Designing the Course Architecture
Based on this analysis, I began planning a parallel course system that would showcase my academic journey with the same elegance as the blog system.
Enhanced Metadata Structure
For courses, the frontmatter needs to capture academic-specific information:
---
title: 'Advanced Topics in Artificial Intelligence'
code: 'IIC3692'
semester: 'Fall 2023'
credits: 10
summary: 'Deep dive into cutting-edge AI research including RAG systems, LLMs, and practical implementations.'
category: 'Data Science and AI'
relevance: 'xl'
technologies: ['Python', 'PyTorch', 'LangChain', 'OpenAI API']
prerequisites: ['IIC2613 - Artificial Intelligence']
---This structure captures both academic context (codes, credits, prerequisites) and professional relevance (technologies, project outcomes).
Hierarchical Organization
Unlike blog posts which are chronologically ordered, courses need categorical organization reflecting the knowledge graph structure:
- Basic Engineering - Foundational mathematics and physics
- Introductory Computer Engineering - Programming fundamentals
- Core Computer Engineering - Essential CS concepts
- Theoretical Computer Science - Mathematical foundations
- Data Science and AI - Specialization area
- Software Engineering and Development - Practical skills
- Information Technology and Management - Business context
Advanced Topics in AI: The Perfect Starting Point
IIC3692 - Advanced Topics in Artificial Intelligence represents an ideal course to begin with for several reasons:
Current Relevance
This course directly connects to the DIIO opportunity and RAG system development, making it immediately applicable to my career goals.
Rich Project Portfolio
The course involved multiple substantial projects:
- RAG System Implementation - Directly relevant to DIIO's needs
- LLM Fine-tuning Experiments - Understanding model customization
- Multimodal AI Applications - Exploring beyond text processing
- Research Paper Analysis - Critical evaluation of cutting-edge techniques
Technology Stack Showcase
The course provided hands-on experience with industry-standard tools:
- LangChain for RAG pipeline development
- Vector Databases (Pinecone, FAISS) for similarity search
- OpenAI APIs for LLM integration
- PyTorch for custom model development
- Streamlit for rapid prototyping and demos
Strategic Documentation Approach
Rather than rushing into RAG implementation, today's planning session revealed the importance of systematic documentation:
Project Context Setting
Each course page will provide:
- Learning objectives and how they were achieved
- Project outcomes with technical details and business value
- Technology decisions and their rationale
- Challenges encountered and solutions developed
Professional Narrative
The course documentation will demonstrate:
- Progression from theory to practice - How academic concepts translate to real applications
- Problem-solving methodology - Approach to complex technical challenges
- Continuous learning - Adaptation to emerging technologies and frameworks
Portfolio Integration
Course pages will link back to:
- Knowledge graph visualization - Showing course relationships
- Blog development journey - Technical implementation stories
- GitHub repositories - Code demonstrations and project outcomes
RAG Project Planning
Tomorrow's RAG implementation will be documented as part of the Advanced Topics in AI course page, demonstrating:
Academic Foundation
- Understanding of attention mechanisms and transformer architectures
- Knowledge of information retrieval and similarity search algorithms
- Experience with prompt engineering and LLM optimization
Practical Implementation
- End-to-end RAG pipeline development
- Vector database integration and management
- API design for scalable retrieval systems
Business Application
- Connection to DIIO's sales automation needs
- Understanding of enterprise AI system requirements
- Consideration of performance, accuracy, and cost trade-offs
Lessons in Content Architecture
Today's analysis reinforced several important principles:
Consistency Enables Scale
The blog system's success comes from consistent patterns that make adding new content straightforward. Replicating this for courses ensures maintainable growth.
Metadata Drives Discovery
Rich frontmatter enables powerful filtering, sorting, and relationship mapping - essential for academic content that needs multiple organizational schemes.
Integration Amplifies Value
Course documentation gains value when integrated with the knowledge graph, blog narrative, and overall portfolio story rather than existing in isolation.
The foundation is now set for comprehensive course documentation that will transform my academic transcript into a compelling professional narrative. Starting with Advanced Topics in AI tomorrow will establish the pattern for documenting my entire Computer Engineering journey.
Next: Implementing the RAG system for Advanced Topics in AI while establishing the course documentation framework.