Build AI-Powered Mobile Apps with RAG, KMP & Flowise

About this course

What if your mobile app could answer questions using your own documents — on Android, iOS and Desktop, with a backend you fully control?

That's exactly what this course teaches. By the end, the theory makes sense, the backend is live, and a real AI-powered app runs on Android, iOS and Desktop.

This course is for mobile developers who want to build AI-powered apps — without a background in machine learning.

If you know how to build Android or iOS apps and you've been watching the AI wave from the sidelines wondering how to get involved, this course is what you need. Every concept is explained from first principles, and every theory lecture is followed by hands-on implementation with real tools and code.

What you will learn

The theory — explained for developers, not researchers

What tokens, context windows and hallucinations actually are — and why they matter for mobile apps

The full RAG framework landscape: LangChain, LlamaIndex, Haystack, DSPy, LangGraph, Flowise, Langflow, Dify, and Firebase Genkit

How to compare LLMs across DeepSeek, Gemini, Claude, GPT, Grok, and local Ollama models using OpenRouter

How RAG (Retrieval-Augmented Generation) works end to end, from document ingestion to LLM response

What vector embeddings are, how similarity search works, and how to choose the right embedding model from the MTEB leaderboard

Chunking strategies — Fixed-Size, Recursive, Document-Specific, and Semantic — and when to use each

Retrieval techniques — Top-K, Similarity Score Threshold, MMR, Hybrid Search, and Reranking

The backend — self-hosted, production-ready

Set up and configure Flowise — a visual RAG pipeline builder — on a real Hostinger VPS

Build Chatflows with document stores, vector search, LLM integration, and custom tooling

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