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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Build the video preprocessing module (segmentation, audio extraction, frame sampling). Integrate Qwen3-VL to generate rich visual descriptions and object detections for each segment. Show how these features are prepared for LlamaIndex.
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.
Multimodal Video Intelligence with Qwen3-VL, GPT-5 & LlamaIndex
Inspired by advancements in long-context multimodal understanding, this challenge tasks you with building a cutting-edge video intelligence system. You will integrate the Qwen3-VL model for robust video and image analysis with GPT-5 for higher-level reasoning and synthesis. The system will leverage LlamaIndex for advanced RAG over multimodal data, allowing it to accurately answer complex 'needle-in-a-haystack' queries spanning long video durations. The core of the system will involve processing entire 30-minute video segments, extracting key visual and auditory information, generating multimodal embeddings, and indexing them using LlamaIndex. An OpenAI Swarm-like orchestration will manage specialized agents that collaborate using an A2A protocol to perform visual search, event detection, and generate comprehensive summaries. MCP could be used to facilitate access to external video processing tools or contextual databases.
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.