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Blogs on practical AI engineering — covering RAG, enterprise LLM architectures, modern web frameworks, and real-world system design.

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🧠✨ AI Blog Repository

Welcome to the blog repository showcasing practical insights and engineering deep dives into AI-driven systems. This repo contains multiple blogs focused on real-world applications of AI, LLMs, and scalable system architectures — now including a broader look at modern web frameworks powering these systems.


📚 Blogs

This blog explores how to build a personalized course recommendation system using Retrieval-Augmented Generation (RAG). We dive into how to semantically index course materials, retrieve relevant data based on user profiles, and generate tailored suggestions using LLMs like OpenAI/GPT or LLaMA.

Key Topics:

  • Vector embeddings & semantic search
  • User profiling and context-aware recommendations
  • LLM integration with real-time retrieval
  • Practical RAG system architecture

This blog covers architectural best practices for building scalable, enterprise-grade LLM-based chatbots. We go beyond basic chat interfaces and dive into reliability, observability, real-time streaming, and MLOps for deployment.

Key Topics:

  • Event-driven architecture with Kafka
  • Streaming responses using SSE/WebSockets
  • ELK for observability & logging
  • Separation of frontend (Next.js), backend (NestJS), and AI layer (Python)
  • Security, tokenization, and context management

Dive into the debates surrounding Next.js in 2025 — from growing complexity and Vercel's ecosystem dominance to real-world migrations and previews of Next.js 16 at the upcoming conference. This piece analyzes whether Next.js still deserves its developer-friendly reputation or if alternatives like Remix, Astro, and SvelteKit are taking the lead.

Key Topics:

  • Next.js complexity & breaking changes
  • Vercel’s monopoly in frontend, cloud, and AI tools
  • i18n, routing, and authentication pain points
  • Real-world migrations (ChatGPT, Northflank, etc.)
  • Next.js 16 and the future of the framework

Stay tuned for more AI infrastructure blogs and real-world implementation guides — spanning LLMs, frontend frameworks, and scalable system design.