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Coding · Intermediate

# Automated model evaluation runner

Evaluate model prompt variations and outputs against structured test datasets and typed criteria

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When something happensPrompt changes pushed
- ThenRun typed evaluation test suite
- ResultEvaluation report with pass rates

Test generative model outputs against standardized datasets using structured validation criteria. The bot runs designated evaluation pipelines, verifies output schemas and assertion constraints, highlights regressions across prompt changes, and formats clear diagnostic reports for development teams.

## Prompt to start with

BotBentoRecommended

```
Execute the evaluation harness against recent prompt or dataset updates in the repository. Compare model outputs against required types, schema rules, and behavioral assertions. Group failures by category and log full input-output traces. Draft a summary report for review, but wait for user approval before posting any comments to pull requests or updating benchmark baselines.
```

Copy

## Tools it uses

GitHubTerminalDatabase

## Works with

BotBento

## Tags

- evaluation
- llm
- testing
- benchmarks
- qa

## Source

[Show HN: jevals – replacing LLM judges with typed Jev decisions](https://news.ycombinator.com/item?id=49780849)
Hacker News · checked 2026-10-15

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-15 · Updated 2026-10-15 · [Suggest a correction](/contact/)

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