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.
Using LlamaIndex, set up a `VectorStoreIndex` with ChromaDB to store mathematical theorems and definitions. Write Python code to load a set of example LaTeX math documents (provided in `data/math_theorems/`) and ingest them into the index. Ensure the text splitting and embedding are optimized for mathematical expressions, potentially using a custom text splitter from LlamaIndex. Provide the necessary `llama_index` imports and ChromaDB client initialization.
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.
Mathematical Proof Assistant
This challenge focuses on building an advanced AI system capable of understanding complex mathematical questions, retrieving relevant theorems and definitions from a specialized knowledge base, and constructing logical proofs or counter-examples. Participants will leverage LlamaIndex's advanced RAG capabilities to ensure contextual understanding and Gemini 2.5 Pro's strong reasoning for generating robust mathematical arguments. The emphasis will be on accurate grounding of facts, verifiable proof construction, and systematic evaluation of the AI's mathematical competence against novel problems. The project requires designing and populating a structured mathematical knowledge base using LlamaIndex data connectors, integrating a vector store like ChromaDB for efficient retrieval. Developers will orchestrate a multi-stage LlamaIndex agent workflow that can plan, execute, and verify proof steps. The final system should demonstrate robust reasoning by generating mathematically sound proofs and identifying valid counter-examples when applicable, similar to the objectives of the 'First Proof' experiment.
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.