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Build AI-Powered Mobile Apps with RAG, KMP & Flowise
Zero to RAG: Build an AI-powered multi-platform app for Android, iOS & Desktop
5.07 reviews27 learners
- Intermediate
- 5h 25m video
- 74 lectures
- English
- Captions
- Updated 2026
Taught by Stefan Jaindl
- 5h 25m of on-demand video
- 74 lectures across 7 sections
- One-off payment — no subscription
- Lifetime access on web and mobile
- Closed captions (English)
Curriculum
7 sections74 lectures5h 25m total
- Introduction & Course Overview2:10
- Introductory Notes1:37
- Download your slides0:05
- Course demo code on github0:07
- Practice Project & Architecture: Intro2:14
- Practice Project4:46
- High-Level Architecture5:20
- RAG Frameworks: Intro1:49
- The RAG Pipeline4:39
- RAG Framework Landscape3:40
- Frameworks: LangChain2:30
- Frameworks: LlamaIndex7:23
- Frameworks: Haystack1:50
- Frameworks: DSpy2:29
- Frameworks: LangGraph3:05
- Frameworks: Flowise1:45
- Frameworks: LangFlow0:58
- Frameworks: Dify.ai0:53
- Frameworks: Firebase Genkit1:19
- Practice Project: Choosing our Framework0:39
- Practice Project: Flowise Local Environment Setup9:25
- LLM Providers: Deepseek, Gemini, Claude, GPT & Grok13:14
- LLM Providers: Comparisons0:09
- Practice: Building a Basic Chat Flow with Gemini10:09
- Local LLMs with Ollama5:15
- LLM Flexibility with OpenRouter1:54
- Practice Project: Using OpenRouter in Flowise7:01
- RAG Fundamentals: Intro2:17
- LLM Foundation: Tokens5:42
- Practice: Byte-Pair Encoding0:13
- Context Windows2:30
- Hallucinations2:57
- RAG: The Truth Filter2:36
- Practice: Setting up the Document Store8:45
- Vector Stores Comparison: Cloud-Managed3:04
- Vector Stores Comparison: Cloud-Managed & Open Source6:27
- Vector Stores Comparison: Traditional Databases3:10
- Vector Stores Comparison: In-Memory & Selection2:50
- Practice: Setting up Qdrant Vector Store4:08
- Vector Embeddings11:22
- Vector Embeddings: MTEB Leaderboard4:04
- Practice: Add Embeddings Model10:29
- Chunking Strategies5:51
- Practice: Improve Document Chunking4:30
- Retrieval Techniques6:21
- Practice: Configuring Qdrant Retriever9:14
- Tooling: Intro2:17
- Practice: CoinGecko API Overview8:27
- CoinGecko API: Course Links0:07
- Practice: Add Real-Time API Data15:37
- Practice: Add Website Scraper Tool4:29
- Practice: Add Calculator & DateTime Tools3:11
- Hosting1:37
- Hosting Options6:31
- Hostinger: Discount Code0:04
- Deploying on VPS6:40
- Test Flowise on sample Website (iFrame)1:43
- Security: Rate Limiting & API Key Protection3:59
- The Mobile RAG KMP Library2:35
- KMP Setup5:33
- Basic Config & API Integration7:16
- Compose UI & Navigation6:19
- The ViewModel & Connecting the Layers6:08
- Data Layer: Handling Streaming (SSE)4:24
- Used Tools & Metadata support5:22
- Sample Questions & Welcome Message2:22
- Adding Desktop support2:34
- Adding Persistance7:42
- Adding Chat Reset Feature2:02
- Additional Configuration3:37
- Securing Calls with API Key4:43
- Distribution: Publishing to Maven9:19
- Integrating Maven Artifact in Host App5:14
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 wonder…
Before you start
- Some prior experience in mobile app development (preferably Android) is expected
- No prior AI knowledge required
Who it’s 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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