RAG

Posts tagged RAG

Showing 3 articles on RAG. From fundamental concepts to advanced patterns.

About this topic

Explore all posts tagged with RAG. 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 RAG.

Our learning methodology emphasizes hands-on building, code execution, and architectural design patterns. When studying topics like RAG, 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.

Series

Building a RAG Chat System: From Zero to Production

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.

30mMay 11, 2026Updated#AI

Series

Understanding the Production RAG Stack: Streaming and Citations

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.

20mMay 19, 2026Updated#AI

Series

How AI Summaries Avoid Hallucination in Content Extraction

How the AISummary component works — content extraction, prompt engineering for factual extraction, truncation strategy, caching, post-type detection, and when to skip generation.

14mMay 25, 2026#AI

Technical Taxonomy & Learning Pathways

We organize our articles, guides, and learning paths using a technical taxonomy designed to facilitate sequential skill acquisition. Topics like RAG 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.