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 18 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Your first task is to initialize your AI SDK project and integrate Gemini 2.5 Pro for multimodal content generation. Set up a basic streaming chat endpoint.
```typescript
import { createOpenAI } from '@ai-sdk/openai';
import { createGoogleGenerativeAI } from '@ai-sdk/google';
import { streamText } from 'ai';
const google = createGoogleGenerativeAI({ apiKey: process.env.GOOGLE_API_KEY });
const model = google('gemini-2.5-pro');
export async function POST(req: Request) {
const { messages } = await req.json();
const result = await streamText({
model: model,
messages,
// You'll need to add tools for ad generation here later
});
return result.to Response();
}
```
Expand this to include a simple tool for 'generateAdContent' that takes user preferences and a conversational context, and returns a multimodal ad draft (text, image URL).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.
Ethical Ad Personalization Agent
This challenge focuses on building a cutting-edge, ethically-aware ad personalization and delivery system for conversational AI platforms. Leveraging Vercel's AI SDK, developers will design an agent that dynamically generates and filters advertisements based on user context, preferences, and real-time conversation flow, while strictly adhering to a defined set of ethical guidelines. The system must integrate Google's Gemini 3 Pro for multimodal ad content generation and personalization, and use Fiddler AI for continuous monitoring and evaluation of ad compliance against ethical policies. Real-time inference capabilities will be supported by RunPod for specialized ad rendering models, and LiveKit will enable voice-interface interactions for a seamless user experience. This challenge emphasizes responsive, context-aware ad delivery combined with robust ethical governance in generative AI applications. Developers will master the intricacies of creating reactive AI interfaces with streaming capabilities, orchestrating multiple generative models, and implementing an automated observability pipeline for ethical AI compliance, moving beyond simple content generation to intelligent and responsible content curation.
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.