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 1 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.
Integrate VAPI into your frontend to enable real-time voice input and output. When a user speaks, send the transcribed text to your AI SDK backend. The AI assistant should process the command (e.g., 'add item to cart', 'find recipe') and respond verbally through VAPI. Ensure smooth conversation flow and error handling. You should include client-side code for initiating the VAPI session.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Real-Time Localized E-commerce Assistant
Develop a cutting-edge real-time e-commerce assistant designed to facilitate the international expansion of grocery delivery services. This challenge focuses on generating dynamic, localized product descriptions, marketing copy, and providing a voice-enabled shopping experience for new markets like Spain and France. The assistant must intelligently adapt content based on regional linguistic nuances and cultural preferences, utilizing the Vercel AI SDK for its streaming capabilities and efficient interaction with large language models. Participants will build a serverless AI backend that interacts with a product catalog stored in AWS DynamoDB and leverages Claude 3.5 Haiku for rapid text generation and translation. The frontend interaction will demonstrate a voice-driven user interface using VAPI, showcasing how modern generative AI can create seamless, personalized e-commerce experiences across diverse global markets. Emphasis is placed on creating responsive, scalable, and culturally aware AI solutions.
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.