Scenario
Expected output
Accurate answers to 10 test questions with source citations
Dataset
Scoring rubric
Code is readable, README explains how to run
Efficient prompt design, appropriate context window usage
Handles edge cases: no results found, ambiguous questions
Answers match ground truth for all 10 test questions
Clean separation of retrieval and generation, sensible chunking strategy
Language-free evaluation
Build your solution in any language or framework — Python, TypeScript, Go, Rust, Java, C#, or anything else. The dataset artifacts may be in one language; your implementation does not need to match. TryCrucible evaluates the behaviour of your system, the quality of your AI workflow, your verification strategy, and the reproducibility of your submission — not your language choice.
Submission requirements
- A public GitHub repository link
- A Dockerfile in the repo root — any language or framework; the evaluator builds and runs your container
- Your solution reads test_inputs.json from the working directory and writes results.json — standard I/O contract across all challenges
- A decisions.md — 3–5 sentences on the key architectural and AI-workflow choices you made
- The system must be fully reproducible — we clone, build, and run it against real test inputs
Evaluation contract
When you submit, the evaluator runs these steps in order:
- 1Clone your public GitHub repository
- 2Build your container from the Dockerfile in the repo root
- 3Mount test_inputs.json into the working directory
- 4Run your solution in a network-isolated sandbox (5 min limit, 512 MB RAM)
- 5Read results.json from the working directory
- 6Score correctness against hidden ground truth, then score architecture, AI workflow, robustness, and clarity
Input (provided by evaluator)
// test_inputs.json
[
{ "id": "t1", "input": { ... } },
{ "id": "t2", "input": { ... } }
]Output (written by your solution)
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