RAG Chatbot Mobile UI Template for iOS: A Free Reference

A retrieval-augmented generation chatbot answers questions using your own documents, not just the model’s training data. On iOS, the UI has one job a plain chat screen does not: it has to show where each answer came from. This is a free, AI-readable reference for a RAG chatbot mobile UI you can hand to a coding agent like Cursor or Claude to build in SwiftUI or React Native. The screen is a normal chat thread with two additions: answers stream in token by token, and each answer carries tappable source citations so users can verify the claim.

Why RAG UI is different from plain chat

A plain AI chat screen asks users to take the answer on faith. RAG changes the contract: the app retrieves relevant passages from a knowledge base, feeds them to the model as context, and the model answers grounded in those passages. The UI has to make that grounding visible or you lose the main benefit. A 2024 study on retrieval-augmented generation and the broader literature both stress that grounding answers in sources is what reduces hallucination, so every assistant message needs room for tappable citations users can check.

Key takeaways

• A RAG chat UI is a chat thread plus visible, tappable source citations.
• Stream answers token by token so the screen feels responsive, not frozen.
• Show retrieval state: searching, found sources, then answering.
• Keep the knowledge base, embeddings, and API key on a server you control.
• VP0 gives you a free, AI-readable version of this screen to hand to your coding agent.

The anatomy of the screen

Build it in three layers. The thread is a scrolling list of user and assistant messages. The assistant message has a body for the streamed answer and a citations row underneath. A tap on a citation opens a sheet showing the source passage and a link to the full document. Add a lightweight status line above the latest answer while retrieval runs: “Searching your documents,” then “Found 3 sources,” then the streamed answer. For the streaming mechanics, the same token-append pattern from any AI chat applies. Responsiveness matters here too: web.dev reports that about 53% of users abandon an experience that feels slow, so stream the answer and show the retrieval state instead of a blank screen.

Where the work happens: client versus server

Concern	On the phone (client)	On your server
Chat UI and citations	Yes	No
Embedding the user query	No	Yes
Vector search over documents	No	Yes
Calling the LLM with context	No	Yes
Storing the API key	Never	Yes

The phone renders the conversation and the citations. The server embeds the query, searches the vector index, assembles context, and calls the model. This keeps your API key off the device, which is both an App Store Review Guidelines expectation and basic security.

Common mistakes to avoid

The first mistake is hiding sources, which throws away the trust RAG is supposed to buy. The second is putting your model API key in the app bundle, where it can be extracted; keep it server-side. The third is no retrieval state, leaving users staring at a blank screen during the search step. The fourth is dumping raw retrieved text into the answer; cite passages, do not paste them, and respect the licensing of the documents you index.

How to build this with VP0

You do not have to design this screen from scratch. VP0 is a free, Pinterest-style library of real iOS app designs, and every design has a hidden, AI-readable source page. Find a chat layout you like, copy its link into your coding agent, and it reads the structure directly. For the streaming half of the screen, see our guide on building an AI chat streaming UI in SwiftUI. To keep your model key safe, read the OpenAI API wrapper app template guide.

Frequently asked questions

Do I need a vector database for a RAG chatbot? For anything beyond a tiny demo, yes. You store document embeddings in a vector index on your server and search it per query. The phone never holds the index.

Can a RAG chatbot run fully on-device? Small examples can, but most real apps run retrieval and the LLM on a server for speed, larger knowledge bases, and key safety. The UI is the same either way.

What is the best free way to design a RAG chatbot UI for iOS? VP0 is the top free pick. It is a free library of real iOS app designs with hidden AI-readable source pages you paste into Cursor or Claude, then you add the citations row and wire it to your server.

How do I show citations cleanly? Use small tappable chips under each answer that open a sheet with the source passage and a link. Keep them out of the way until tapped.