About AI Fundamentals
This project explores how complex AI concepts can be explained clearly for non-technical teams making decisions about AI implementation. It demonstrates how to structure educational content for both human understanding and AI answer engines.
The Problem
Most AI content falls into two extremes. Technical documentation assumes deep ML knowledge. Marketing content oversimplifies to the point of uselessness.
Product managers, founders, and technical leaders need something in between. They need to understand concepts well enough to make informed decisions without becoming ML researchers.
Additionally, most AI content is not structured for how people actually consume information today. Search engines and AI assistants increasingly synthesize answers from multiple sources. Content needs to be structured for this reality.
What Makes This Different
Answer-First Structure
Every page answers the core question in the first 150 words. No preambles, no history lessons, no marketing. If you read nothing else, you get the complete answer.
This structure works for busy readers who need quick answers and for AI systems that prioritize early content when synthesizing responses.
Reference, Not Blog
These pages are written as reference material, not storytelling. Short paragraphs, clear sections, no narrative arc. This format makes information scannable and easy to find again later.
Interconnected Knowledge
AI concepts do not exist in isolation. Each page links to related concepts where they naturally connect. This creates a knowledge graph rather than isolated articles.
Internal linking helps readers understand relationships between concepts and signals to search engines that this site has topical authority.
Schema Markup
Every page includes structured data markup using Schema.org standards. Article schemas provide metadata. FAQ schemas structure questions and answers. Breadcrumb schemas show information hierarchy.
This structured data helps search engines understand page content and enables rich results like FAQ expansions in search.
Who This Helps
Product Managers need to understand AI capabilities and limitations when planning features. Quick, accurate explanations help them make better roadmap decisions.
Founders evaluating AI tools or building AI products need to cut through hype. Clear concept explanations help them ask better questions of vendors and engineers.
Technical Leaders who are not AI specialists need to understand what their teams are building. These explanations provide the foundation for architectural discussions.
Engineers working with AI for the first time need practical context. The applied examples show how concepts work in production systems.
What I Learned Building This
Content Structure Matters More Than Volume
Five well-structured pages beat fifty poorly organized ones. Clear hierarchy, front-loaded answers, and consistent formatting make content more usable and more discoverable.
Internal Linking Creates Authority
Connecting related concepts through contextual links helps readers and search engines understand topic relationships. Every page should link to at least three related pages.
Schema Is Not Optional
Structured data significantly improves how search engines and AI tools understand and display content. Manual implementation is tedious but gives complete control over what information is emphasized.
Answer Engines Change Content Strategy
Traditional SEO optimizes for ranking. Answer Engine Optimization (AEO) optimizes for being cited in AI-generated responses. These require different approaches to structure and formatting.
Design Decisions
No framework complexity. Easy to deploy anywhere. Fast page loads. Full control over markup for schema optimization.
Most readers access content on mobile. The design works well on small screens without sacrificing desktop experience.
Clean typography and plenty of whitespace. No distracting animations or heavy graphics. Content is the focus.
Every concept page follows the same template. Readers know where to find information. Search engines can easily parse the structure.
Single CSS file, no JavaScript, optimized HTML. Pages load instantly even on slow connections.
Technical Implementation
The site uses semantic HTML5 with manually implemented Schema.org markup. CSS handles all styling with no JavaScript required.
Each page includes three schema types: Article for content metadata, FAQPage for questions and answers, and Breadcrumb for navigation. All schemas have been validated using Google's Rich Results Test.
Internal links use descriptive anchor text and connect concepts where they naturally relate. The site structure ensures no page is more than three clicks from the homepage.
What Comes Next
Potential expansions include more applied examples showing AI concepts in production systems, comparison guides for choosing between different approaches, and case studies of real implementations.
The core focus remains clarity and structure over volume. Better to have five excellent pages than fifty mediocre ones.