Building Retrieval-Augmented Generation (RAG) pipelines directly inside Android apps sounds like the ultimate privacy and offline-first solution. However, running local LLMs, embeddings, and vector databases on commercial mobile devices quickly runs into a wall of hard hardware constraints - including severe memory limits, high latency, aggressive battery draining, and data freshness challenges. Instead of showing polished marketing demos or cloud-centric architectures, this circle discussion is a reality check on native mobile AI. Let's talk about practical experiences regarding what happens when a local model like Gemma chokes device memory, how to handle vector searches without destroying the user experience, and where developers draw a strict line between on-device processing and cloud handoffs.