Showing 6 articles on AI. From fundamental concepts to advanced patterns.
Explore all posts tagged with AI. Each article in this collection covers core concepts, practical implementations, and real-world patterns to deepen your understanding.
Whether you are just getting started or looking to master advanced techniques, these curated posts provide the depth and clarity needed to build production-grade skills in AI.
Our learning methodology emphasizes hands-on building, code execution, and architectural design patterns. When studying topics like AI, we recommend starting with the fundamental principles, working through our step-by-step tutorials, running the interactive code cells in the browser, and applying these concepts directly to the hands-on projects listed in our project catalog.
All code examples on this page are fully tested and validated against modern standards. If you run into issues or have questions while working through the articles, you can use our in-browser AI Assistant to get instant, context-aware support grounded in our verified repository files.
Every developer has a blog in 2026. So why did I spend six months building my own? Because I wanted to teach things that Medium, Dev.to, and WordPress cannot support — interactive code, RAG chat, gamification, and a learning engine that rewards understanding, not scrolling. This is the architecture story.
Building a production RAG chat system from scratch using OpenAI embeddings, pgvector HNSW indexing, and hybrid search. Explains how the Ask AI page works: OpenAI text-embedding-3-small generates 1536-dimensional embeddings stored in PostgreSQL with pgvector and HNSW indexes for fast approximate nearest-neighbor search, combined with PostgreSQL tsvector full-text search using weighted BM25 scoring, relevance thresholds for preventing hallucination, source-grounded citations showing which post each answer is from, and the complete API pipeline from query to streaming SSE response with session management and rate limiting.
The Ask AI page streams answers live via SSE, shows clickable source badges before the first token arrives, auto-retries on connection drops, enforces daily quotas via Redis, and integrates with chat sessions and XP gamification.
How the AISummary component works — content extraction, prompt engineering for factual extraction, truncation strategy, caching, post-type detection, and when to skip generation.
How this blog optimizes for AI crawlers — llms.txt, ai-profile.json, structured data (TechArticle, FAQ, HowTo, Course, BreadcrumbList), speakable markup, articleBody for crawlers, and robots.txt for AI bots.
AI-powered mock interviews that generate questions from a user's reading history, scale difficulty based on performance, and provide structured feedback with scoring.
We organize our articles, guides, and learning paths using a technical taxonomy designed to facilitate sequential skill acquisition. Topics like AI are mapped to key development tracks including full-stack web engineering, data pipelines, system architecture, and machine learning development.
By centering our content around key technical tags, you can easily pivot from reading theoretical tutorials to working on interactive sandbox labs and solving daily practice challenges. We update our curriculum weekly, introducing fresh deep-dives, industry best practices, and production-tested patterns to help you stay ahead in software engineering and data science.
