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.
Implement an 'Image Analysis Agent' that uses GPT-5 (or a specialized VLM integrated with GPT-5) to analyze a base64 encoded patent figure. This agent should extract key features, objects, and their relationships described in the image. Use MCP to mock calling a 'Getty Images API' or similar service if GPT-5's multimodal capabilities are not sufficient for complex diagram interpretation. The output should be structured textual descriptions suitable for feeding into the graph RAG.
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 Patent & IP Novelty Detector
Develop a cutting-edge AI system for patent and intellectual property (IP) research.. This challenge focuses on building an agentic system that can analyze patent documents (text and images) to determine novelty, identify prior art, and summarize key innovations. The system will leverage GPT-5's advanced reasoning and multimodal capabilities combined with a graph-based RAG architecture. Your solution will involve a custom agent architecture designed to perform specialized tasks: a 'Patent Search Agent', an 'Image Analysis Agent', and a 'Novelty Assessment Agent'. It will employ a graph-based RAG system to interlink patent texts, claims, and associated images, allowing for deeper contextual understanding. MCP-enabled tool integration will facilitate connecting to external patent databases and image recognition APIs, as well as a simulated Getty Images API for multimodal content retrieval and analysis.
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.