Python & AI Learning Platform

Problem

Software developers, AI engineers, and data analysts face fragmented, surface-level learning resources. Most guides cover hello-world LLM concepts but skip the edge cases, structural design decisions, and real-world deployment challenges of pipelines like RAG. Static docs lack interactive search, forcing engineers to dig through forums and repos for verified patterns.

Approach

• ◆Structured Learning: Built series-based learning paths that teach technical concepts progressively.
• ◆AI Assistant Integration: Integrated a custom production-grade AI Assistant powered by a localized RAG pipeline.
• ◆Used Next.js for server-rendered page speed and Tailwind CSS for the visual system.
• ◆Connected a secure FastAPI backend with custom execution runtime test runners.

Results

• ✓Organic ranking on high-intent AI and engineering topics
• ✓80% lower user search latency through grounded RAG answers
• ✓Low TTFB for bots and humans through CI and cache warming

Gallery

Stack

• Next.js
• TypeScript
• Tailwind CSS
• FastAPI
• RAG
• Vector DB

Links

• Live demo

Sources / further reading

• LangChain Documentation
• Next.js App Router Documentation
• Retrieval-Augmented Generation (RAG)