Pattern: LLM In Frontend

LlmInFrontend is an architectural approach for building LLM-powered applications where the frontend owns the AI experience: prompts, orchestration, and even OpenAI SDK usage all live in the browser. The backend’s job is to securely proxy OpenAI requests and expose business data, but it no longer blocks rapid iteration on prompts or AI flows.

See also: LlmInBackend.

When to Use LlmInFrontend

Use the LlmInFrontend pattern when:

• You want to enable rapid iteration on prompts, AI flows, and user experience without waiting for backend deploys.
• Your product requires frequent experimentation or close collaboration between frontend and product teams.
• The majority of your AI logic and orchestration can safely run in the browser, and only secrets or sensitive operations need to stay server-side.
• You want to empower frontend developers to own the AI experience and move quickly.

Avoid this pattern if:

• You need to keep all AI logic, prompts, or orchestration confidential or tightly controlled for compliance or security reasons.
• Your application requires strict server-side enforcement of business logic, rate limits, or data privacy that cannot be handled by a proxy alone.

LlmInFrontend is best for teams and products that value speed, flexibility, and a modern, collaborative workflow between frontend and backend roles.

How LlmInFrontend Works

In this model, the UI isn’t just a thin client. The frontend manages prompts, orchestrates AI logic, and controls the flow of interaction. The backend acts as a secure gatekeeper, proxying requests and keeping secrets safe.

Advantages

• Speed: Frontend teams can experiment and ship AI features fast, without waiting for backend deploys.
• Real-time iteration: Prompts and AI flows can be tuned and improved instantly.
• Separation of concerns: The backend focuses on security and business data, while the frontend owns the user experience and AI orchestration.

Disadvantages

• Security risk: If too much moves to the frontend, you risk exposing sensitive operations. Rate limiting, access control, and security must be handled robustly in the backend proxy.

Expanded Architecture

Here’s what a typical LlmInFrontend architecture looks like in practice:

Summary

LlmInFrontend architecture is ideal for teams that want to move fast with LLMs, empower frontend developers, and keep sensitive operations secure. If you want to ship better AI features, faster, with a leaner backend, LlmInFrontend is a proven approach.