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Operations · Intermediate

# Curating and scoring conversation logs for model evaluation

Evaluate large volumes of prompt-response pairs, tag quality issues, and filter datasets for training

[Use with BotBento ](https://app.botbento.com/?ref=usecases&utm_source=botbento.com&utm_medium=usecases&utm_campaign=curating-and-scoring-conversation-logs-for-model)Copy promptShare[Markdown](/text/usecases/curating-and-scoring-conversation-logs-for-model/index.md)

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

- When you askProvide dataset source and scoring rules
- ThenEvaluate conversations and label tags
- ResultCurated dataset draft ready for review

Inspect massive batches of conversational text logs to detect unwanted behaviors, label dialogues, and extract clean data subsets. The bot evaluates prompts and completions against custom criteria, tags issues, and prepares revised datasets for review.

## Prompt to start with

BotBentoRecommended

```
Examine conversational logs pulled from the specified database or files. Score each conversation based on criteria such as sycophancy, adherence to instructions, and factual accuracy. Apply descriptive labels and flags to low-performing interactions. Generate a cleaned export draft with problematic entries removed or tagged. Never delete any records or overwrite existing datasets without explicit user confirmation.
```

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## Tools it uses

DatabaseSpreadsheetTerminal

## Works with

BotBento

## Tags

- mlops
- data-curation
- evaluation
- datasets

## Source

[Show HN: We built an AI tool for working with massive LLM chat log datasets](https://news.ycombinator.com/item?id=45981930)
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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