Integrate it by putting a thin “chat API” layer between your mobile app and your RAG provider: the app sends messages to your backend, your backend calls the RAG chat-completions endpoint, then streams or returns the response to the app with citations. This keeps API keys off devices and improves security and control.
In CustomGPT, you typically call the chat completions endpoint for your agent/project and session to get grounded answers from your indexed content.
For production mobile apps, avoid calling vendor APIs directly from the client—OWASP flags authentication/token handling as a major API risk area, and “key-in-app” patterns are easy to leak.
What does the minimum architecture look like?
A safe, standard setup:
- Mobile app (iOS/Android): UI + local state only
- Your backend: auth, rate limits, logging, prompt/guardrails
- CustomGPT API: retrieval + answer generation (grounded responses)
What should I send in each request?
Send:
- user_message
- session_id (conversation continuity)
- optional context: user_id, locale, app_version
- optional policy flags: “citations required”, “refuse if not found”
CustomGPT supports “send a message to a conversation” style chat-completions flows (project + session scoped).
Should my mobile app call CustomGPT directly, or through my backend?
Use your backend in almost all real deployments.
| Option | Pros | Cons | Best for |
|---|---|---|---|
| Direct-from-mobile → CustomGPT | Faster to prototype | API key exposure, less control | Demos / internal prototypes |
| Mobile → Your backend → CustomGPT | Secure keys, better governance, analytics, rate limiting | More engineering | Production apps |
OWASP API Security guidance strongly warns about broken authentication patterns and token compromise risks—backend mediation reduces that attack surface.
Should I stream responses to mobile (typewriter effect), or return in one payload?
- Stream if you want faster perceived latency and chat UX
- Single response if you need simpler networking + caching
Either way, keep the “final answer + citations” structure consistent for trust and debugging.
(CustomGPT’s API is designed around conversation/message interactions; your backend can pass-through streaming if supported by your chosen HTTP stack.)
What security controls matter most for mobile + RAG?
Prioritize:
- No API keys in the client
- User auth + short-lived tokens
- Server-side rate limiting
- Request logging & redaction
- Permission-aware retrieval (only show what this user can access)
OWASP mobile/auth guidance emphasizes secure session handling and token lifecycle management (e.g., deleting tokens on logout, invalidating refresh tokens).
How do I implement this with CustomGPT specifically?
- Create/choose your CustomGPT agent and ensure your content is ingested and up to date.
- Generate an API key and store it only on your server.
- Backend endpoint: /chat
- validates your app user
- maps user → session_id
- calls CustomGPT chat completions endpoint (project + session)
- Return: answer, citations/sources, confidence (if you track it), and optional follow_ups.
If you want quicker integration, CustomGPT also publishes an SDK-focused approach to embed chatbot functionality into applications.
What’s a production-ready “API contract” for my mobile app?
Use a simple schema your UI can trust:
Request
- message: string
- sessionId: string
- context: { userId, orgId, locale }
Response
- answer: string
- sources: [{ title, snippet, url_or_doc_id }]
- followUps: string[]
- safety: { refused: boolean, reason?: string }
This makes it easy to render citations, handle “not found,” and avoid silent hallucinations.
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Frequently Asked Questions
How do I integrate a custom RAG chatbot via API into my mobile app?
Integrate it by placing a secure backend layer between your mobile app and the RAG provider’s API. The mobile app sends user messages to your backend, the backend authenticates the user, calls the RAG chat endpoint, and returns a grounded response with citations. CustomGPT supports chat-completions style API calls scoped to your agent and session, allowing you to deliver source-backed answers securely.
Should my mobile app call the RAG API directly or go through my backend?
In production environments, always route calls through your backend. Direct-to-vendor calls expose API keys and reduce governance control. A backend layer protects credentials, enforces rate limits, logs activity, and applies guardrails. CustomGPT API keys should be stored server-side only to prevent client-side leakage.
What does the minimum secure architecture look like?
A standard architecture includes a mobile app for UI and local state, a backend server for authentication and policy enforcement, and the CustomGPT API for retrieval and answer generation. This separation protects sensitive credentials and allows you to control how responses are handled before they reach users.
What data should I send in each API request?
Each request should include the user’s message, a session ID for conversation continuity, and optional context such as user ID or locale. You may also include policy flags like requiring citations or refusing unsupported answers. CustomGPT supports session-based conversations so responses remain grounded and context-aware.
How should responses be structured for mobile apps?
Responses should include the answer text, structured source citations, optional follow-up suggestions, and a safety indicator if the system refuses to answer. CustomGPT returns grounded responses that can be formatted into a consistent API contract for reliable mobile rendering.
Should I stream responses to mobile or return a single payload?
Streaming improves perceived responsiveness and enhances chat experience, while single-payload responses simplify networking and caching. Both approaches work, but the final answer should always include citations for transparency. CustomGPT’s conversation-based API supports structured answer delivery that your backend can stream or return in full.
What security controls are most important for mobile RAG integrations?
The most critical controls include never storing API keys in the mobile client, using short-lived authentication tokens, implementing server-side rate limiting, logging requests securely, and enforcing permission-aware retrieval. CustomGPT supports permission-scoped retrieval so users only see content they are authorized to access.
How do I manage conversation sessions securely?
Map each authenticated app user to a server-managed session ID and avoid exposing internal identifiers to the client. Store session state on your backend and forward only necessary identifiers to CustomGPT. This ensures continuity without exposing system-level configuration.
How do I prevent hallucinations in a mobile AI chatbot?
Prevent hallucinations by enforcing grounded answering rules such as requiring citations and refusing responses when evidence is missing. CustomGPT allows you to configure source-grounded responses so answers are based only on indexed content.
What does a production-ready API contract look like?
A production-ready contract should include a clear request structure with message and session ID fields, and a structured response containing answer text, citations, follow-up suggestions, and safety flags. CustomGPT responses can be normalized into this format to support consistent mobile rendering.
How is CustomGPT deployed inside a mobile application environment?
Deployment involves creating or selecting your CustomGPT agent, ingesting and indexing your content, generating an API key stored securely on your backend, and building a server endpoint that forwards validated chat requests to CustomGPT’s chat-completions API. This approach ensures mobile users receive grounded, secure responses.
How do I ensure scalability for a mobile RAG chatbot?
Scalability requires backend rate limiting, monitoring, session management, and structured logging to maintain performance and governance. CustomGPT supports production-grade usage patterns so organizations can scale mobile AI experiences without compromising answer reliability.