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AI · API Engineering

LLM API INTEGRATION

Server-side LLM integration that keeps API keys out of the browser while controlling context, cost, and access for each workflow.

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ROLE
Creator · Developer
YEAR
2026
DEPARTMENT
Web App
STATUS
Published
CASE STUDY / 2026
LLM REQUEST PATHUser request → Server policy → Model provider → Checked answerREQUEST / POLICY / MODEL / RESPONSE
04detail sections
04tools
00media assets
BUILT WITH
OpenAI Responses APITypeScriptD1Rate limiting
01 / CASE STUDY

What changed between the brief and the build.

A useful case study explains the problem, the decisions made during the work, and what the finished direction makes possible.

01CHALLENGE

01 · Request

The client sends only a question and scope ID. The server selects approved system instructions and context instead of trusting client-supplied secrets.

02PROCESS

03 · Model

The model receives a short, bounded prompt and is selected according to the quality, latency, and budget required by the task.

03OUTCOME

04 · Validate

The response is checked before delivery, usage is recorded without storing API keys, and quota or provider failures become understandable errors.

02 / LLM REQUEST PATH

A bounded path from question to checked answer.

The model is one service inside the product, with authorization, approved context, limits, and a visible failure path around it.

PROJECT SYSTEM MAPREQUEST / POLICY / MODEL / RESPONSE
WHY THIS SHAPE

Responsibilities stay visible. A future feature can be placed in the correct layer before implementation, reducing accidental coupling and making reviews easier.

04 / FULL WALKTHROUGH

The decisions behind the result.

These sections are stored with the project and can be expanded, rewritten, or reordered from Creator Studio as the case study develops.

  1. 01

    01 · Request

    The client sends only a question and scope ID. The server selects approved system instructions and context instead of trusting client-supplied secrets.

  2. 02

    02 · Guardrails

    Authorization, prompt length, conversation depth, and hourly requests are limited, with private data removed before the model call.

  3. 03

    03 · Model

    The model receives a short, bounded prompt and is selected according to the quality, latency, and budget required by the task.

  4. 04

    04 · Validate

    The response is checked before delivery, usage is recorded without storing API keys, and quota or provider failures become understandable errors.

MAINTAINABILITY

Decisions stay explainable.

Architecture and implementation details are documented as part of the work, not reconstructed after delivery.

MEDIA

Proof lives beside the story.

Cover art, screenshots, diagrams, and demo video can be updated without changing the project record or page structure.

GROWTH

Ready for the next iteration.

Clear layers and reusable patterns make future features easier to estimate, review, and deliver safely.

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