eval_752
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      • Reproducibility

      One of eval_752's design principles is that every evaluation should be reproducible. If you share a result, the recipient should be able to verify exactly what happened.

      What .eval752.zip Captures

      When you export a run, the bundle includes everything needed to understand and reproduce the evaluation:

      IncludedWhat it is
      Dataset snapshotThe exact items that were evaluated, frozen at export time
      Run configurationProvider, model name, parameters, scoring method
      Per-item resultsEach item's model response, score, latency, and error details
      Metadataeval_752 version, timestamps (UTC), environment info
      AssetsEmbedded images and files referenced by items

      This means the recipient doesn't need access to your eval_752 instance, your Hugging Face account, or your provider credentials to review what happened.

      What It Doesn't Capture

      Some things are inherently non-reproducible:

      • Model state: LLM providers can update or swap models at any time. The same gpt-4o you tested today might behave differently tomorrow. The bundle captures what the model said, not which exact weights were running.
      • Latency conditions: Network latency, rate limiting, and server load vary between runs. Latency data in the bundle reflects the original test conditions.
      • Provider availability: A provider endpoint might disappear or change its API contract after you export.

      Re-importing a Bundle

      An exported .eval752.zip can be imported into another eval_752 instance. The import creates:

      • A dataset (if only the dataset portion is included)
      • A completed run with all results (if results are included)

      This is useful for:

      • Sharing evidence with colleagues who run their own eval_752 instance
      • Archiving runs for compliance or audit
      • Comparing runs across different environments

      Thinking About Evidence Quality

      A high-quality evaluation result has these properties:

      1. Identified: You know which provider, model, and dataset were used
      2. Timestamped: You know when the evaluation ran
      3. Complete: Every item has a response and score, or an explicit error
      4. Inspectable: You can drill into any item and see the raw prompt, response, and scoring decision
      5. Shareable: Someone else can verify the result without trusting your word

      eval_752 bundles are designed to satisfy all five. A screenshot of a dashboard satisfies only the first two.

      For export workflows, see Exporting Results.