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

What you'll learn

Understand how RAG systems work end to end — tokens, embeddings, chunking, retrieval, hallucinations
Build a fully configured RAG backend using Flowise and Qdrant, with real document ingestion, semantic search, and LLM integration
Build and publish a Kotlin Multiplatform library powering Android, iOS and Desktop from a single codebase with SSE streaming and clean architecture
Extend a RAG agent with real-time tools — live API data, web scraping, calculator and DateTime — to answer questions beyond the knowledge base
Compare and evaluate LLMs (DeepSeek, Gemini, Claude, GPT, Grok, Ollama) and embedding models using OpenRouter and the MTEB leaderboard
Deploy a self-hosted Flowise backend on a real VPS with rate limiting and API key protection

Requirements

Some prior experience in mobile app development (preferably Android) is expected
No prior AI knowledge required

Description

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 your on-ramp. 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

Mobile developers who want to add real AI capabilities to their apps and understand RAG systems from the ground up
Mobile/Kotlin Multiplatform Developers who want full control over their AI stack — self-hosted backend, own vector database, no vendor lock-in — rather than relying on managed services
Mobile developers curious about LLMs, vector databases, and RAG who want a practical, hands-on course that goes beyond simple API calls

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