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

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
• Connect Qdrant as the vector database — self-hosted for full data privacy and zero vendor lock-in
• Integrate OpenRouter to switch between LLMs with a single config change
• Add observability and tracing with Langfuse

The mobile app — one codebase, three platforms

• Build a Kotlin Multiplatform (KMP) library that powers Android, iOS, and Desktop from a single codebase
• Implement clean architecture — Data, Domain, UI — following modern Android architecture principles
• Stream AI responses in real time using Server-Sent Events (SSE) — a lightweight alternative to WebSockets
• Navigate with Jetpack Compose Navigation 3
• Ship a complete demo app that queries your self-hosted RAG backend
• Deploy the KMP library on Maven Central

Who this course is for

• Android developers who want to add AI capabilities to their apps without starting from scratch on ML theory
• Mobile developers (Android or iOS background) looking to go multi-platform with KMP while integrating real AI features
• Any developer who wants to understand RAG deeply — not just call an API, but know exactly what’s happening under the hood

What makes this course different

Most AI courses teach you to call OpenAI’s API and call it a day. This course goes further:

• The entire backend runs on your own server — no mandatory cloud subscriptions, no surprise bills, full control over your data
• The theory is taught at the right depth for developers — enough to make architectural decisions confidently, without unnecessary academic detour
• Every framework, database, and LLM choice is explained with real trade-offs — not just “use this because the tutorial says so”
• The final deliverable is a working multi-platform mobile app connected to a live, self-hosted RAG backend