To add an AI feature to an existing app, put the LLM call behind your own backend, never in the client. Your server owns the prompt, retrieves any context (RAG), calls the model, enforces guardrails, streams the response back, and logs everything for evaluation. That single architectural decision — a thin AI service layer between your app and the model provider — is what makes the feature secure, debuggable, and affordable. The model is the easy part; the plumbing around it is the work.
Here's the architecture we use to ship AI features into real products without blowing up the roadmap or the bill.
Where the LLM actually goes
The most common beginner mistake is calling the model API from the frontend. Don't. The model call belongs in a backend service that you control, for four reasons:
- Security — your API keys never reach the browser.
- Control — you own the prompt, the context, and the guardrails.
- Cost — you can cache, rate-limit, and pick the cheapest model per task.
- Observability — you log inputs and outputs to evaluate and debug.
Think of it as a small AI service layer: your app talks to your backend, your backend talks to the model.
The request flow, end to end
A production AI feature handles one request like this:
- Validate & authorize the user request on your backend.
- Retrieve context if needed (RAG over your data) — see RAG vs fine-tuning.
- Build the prompt from a versioned template plus context.
- Call the model, ideally streaming the response.
- Apply guardrails — validate output shape, filter unsafe content.
- Stream to the client and log the full interaction.
Each step is a place to add safety, caching, or fallbacks — which is exactly why it lives on your server.
Stream the response
LLMs are slow by web standards — a full answer can take several seconds. Streaming tokens as they generate turns a frustrating wait into a live, ChatGPT-style experience. Use server-sent events (SSE) or a streaming response from your backend; perceived performance improves dramatically even though total time is unchanged. For a Next.js app, the route handler streams from your AI service straight to the client.
Guardrails are not optional
An AI feature in production needs defensive layers:
- Input limits — cap length and rate to control cost and abuse.
- Output validation — if you expect JSON, parse and reject malformed output (and retry).
- Content safety — filter or block unsafe requests and responses.
- Fallbacks — a graceful message when the model errors or times out.
- A human path — let users escalate when the AI is unsure.
Evaluate, or you're flying blind
You can't improve what you don't measure. From day one, log every prompt and response, and build a small evaluation set of representative inputs with expected outputs. Re-run it whenever you change a prompt or swap a model. This is what separates an AI feature that quietly degrades from one that gets better — and it's cheap insurance against a prompt change silently breaking quality.
Keep the bill under control
LLM costs creep. The levers that matter:
| Lever | What it does |
|---|---|
| Model routing | Use a small/cheap model for easy tasks, a frontier model only when needed |
| Caching | Cache identical or near-identical requests |
| Prompt trimming | Shorter prompts and retrieved context cost less per call |
| Limits & quotas | Per-user rate limits prevent runaway spend |
| Streaming + early stop | Stop generation once you have what you need |
A shipping checklist
- Model calls run on the backend, not the client
- Prompts are versioned templates, not inline strings
- Responses stream to the UI
- Output is validated before it's trusted
- Every interaction is logged
- An eval set guards quality across changes
- Per-user limits and model routing cap cost
Frequently asked questions
Where should the LLM API call happen — frontend or backend? Always the backend. It keeps your API keys safe, gives you control over prompts and guardrails, and lets you cache and rate-limit to manage cost.
How do I make the AI feature feel fast? Stream the response token by token using server-sent events. Total time is the same, but perceived performance is far better than waiting for the full answer.
Do I need RAG to add AI to my app? Only if the feature needs your private or current data. If the task is generic (summarize, rewrite, classify), prompting alone may be enough.
How do I stop AI costs from spiraling? Route easy tasks to cheaper models, cache repeated requests, trim prompts, and enforce per-user rate limits. Log usage so you can see where spend goes.
Want an AI feature shipped into your product the right way? Our AI engineering team does exactly this. Book a free call.