Instruction file imported from chuanqisun/courseek (.github/instructions/project.instructions.md). Copyright stays with the author.

System Architecture

A pipeline for mood board based prodcut designs generation.

Workflow

A Human-AI co-iterative loop. Non-linear progression. End with product requirements documentation (PRD)

Flow control

• Make sure each step only responds to explicit user action within the step

Model access

• Use OpenAI SDK response API for text gen
• Use Together.ai for image gen

OpenAI SDK docs

INPUT

import OpenAI from "openai";

const openai = new OpenAI();

const response = await openai.responses.create({
  model: "gpt-4.1",
  input: "Tell me a three sentence bedtime story about a unicorn.",
});

console.log(response);

OUTPUT

{
  "id": "resp_67ccd2bed1ec8190b14f964abc0542670bb6a6b452d3795b",
  "created_at": 1741476542,
  "status": "completed",
  "model": "gpt-4.1-2025-04-14",
  "output": [
    {
      "type": "message",
      "id": "msg_67ccd2bf17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
      "status": "completed",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "In a peaceful grove beneath a silver moon, a unicorn named Lumina discovered a hidden pool that reflected the stars. As she dipped her horn into the water, the pool began to shimmer, revealing a pathway to a magical realm of endless night skies. Filled with wonder, Lumina whispered a wish for all who dream to find their own hidden magic, and as she glanced back, her hoofprints sparkled like stardust.",
          "annotations": []
        }
      ]
    }
  ]
}

Together.ai docs

Models

• Free model (strict rate limit, use for testing only): black-forest-labs/FLUX.1-schnell-Free
• Low cost model: black-forest-labs/FLUX.1-schnell

INPUT

import Together from "together-ai";

const together = new Together();

const response = await together.images.create({
  model: "black-forest-labs/FLUX.1-schnell",
  prompt: "",
  steps: 3,
});
console.log(response.data[0].b64_json);

OUTPUT

{
  "data": [
    {
      "b64_json": "<base64-encoded-image-data>",
      "revised_prompt": "<revised-prompt-if-applicable>"
    }
  ]
}