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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design the initial LangGraph state and nodes required for the multi-hop reasoning agent. Define the roles of different "agents" or functions within the graph, considering multimodal input. Outline how Gemini 2.5 Pro will be used for initial image analysis and how RAG will be triggered.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.
Multi-Hop Factual Reasoning for VLM with LangGraph & Gemini 2.5 Pro
Develop an advanced agent system leveraging Gemini 2.5 Pro's multimodal capabilities and LangGraph for orchestrating multi-hop reasoning. The primary goal is to accurately answer complex, fact-based questions about images by performing sequential information retrieval, analysis, and synthesis. This challenge emphasizes achieving high factual accuracy and providing transparent, auditable reasoning steps through careful agent design. The system will break down intricate visual queries into simpler sub-problems, using Gemini 2.5 Pro for initial visual understanding and text generation, and a local RAG pipeline with DuckDB as a vector store for contextual knowledge retrieval. Structured prompting with Guidance will be crucial for controlling agent behavior and enforcing reasoning paths, ensuring robustness and reliability in its responses.
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.