Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 16 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Set up a new Next.js project and integrate the Vercel AI SDK. Initialize a client for Gemini 2.5 Pro. Create a basic chat component that can send prompts to the model and display streaming responses in the UI. Ensure your project structure supports future integration of structured output validation and voice features.
```typescript
// app/api/chat/route.ts
import { GoogleGenerativeAI } from '@google/generative-ai';
import { GoogleGenerativeAIStream, Message, StreamingTextResponse } from 'ai';
const genai = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY || '');
export const runtime = 'edge';
export async function POST(req: Request) {
const { messages } = await req.json();
const model = genai.getGenerativeModel({ model: 'gemini-pro' });
const stream = await model.generateContentStream({
contents: messages.map((m: Message) => ({ role: m.role, parts: [{ text: m.content }] })),
});
return new StreamingTextResponse(GoogleGenerativeAIStream(stream));
}
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
AI-Powered Regulatory Drafting Assistant
The US Department of Transportation's use of Gemini to draft federal regulations highlights the potential for generative AI to revolutionize highly structured, text-heavy workflows. This challenge focuses on building an interactive, AI-powered assistant designed to accelerate the drafting of regulatory documents. You will leverage the Vercel AI SDK to create a streaming, real-time user interface, enabling collaborative drafting and incorporating structured feedback mechanisms. The core AI will use Gemini 2.5 Pro for its advanced reasoning and text generation capabilities, specifically focusing on adherence to legal templates and factual accuracy. The solution will integrate tools for validating structured output and providing real-time quality assurance, ensuring compliance and precision in the generated text. This system will not just generate text but act as an intelligent co-pilot, guiding users through complex drafting processes while maintaining a high standard of legal and factual integrity.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.