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Operations · Intermediate

# Uncovering user friction and feature asks in chat transcripts

Identify agent failure patterns, user corrections, and unhandled feature requests from chat logs.

[Use with BotBento ](https://app.botbento.com/?ref=usecases&utm_source=botbento.com&utm_medium=usecases&utm_campaign=uncovering-user-friction-and-feature-asks-in)Copy promptShare[Markdown](/text/usecases/uncovering-user-friction-and-feature-asks-in/index.md)

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

- On a scheduleNightly conversation transcript sync
- ThenCluster user corrections and friction
- ResultReview proposed Linear tickets

Analyze chat and voice session logs to spot user frustration, repeated rephrasing, and explicit corrections. Group related user queries into emerging themes and unhandled capabilities, turning unstructured customer dialogues into structured issue summaries and quality improvement opportunities.

## Prompt to start with

BotBentoRecommended

```
Scan completed conversation logs in the database for friction markers such as repeated rephrasing, prompt corrections, and unmet capability requests. Group recurring patterns into categorized problem clusters with sample dialogue snippets. Prepare a weekly friction report and list suggested action items. Always ask for user confirmation before creating any tracking tickets in Linear or sharing notifications.
```

Copy

## Tools it uses

DatabaseLinearSlack

## Works with

BotBento

## Tags

- analytics
- conversations
- feedback
- support
- llm

## Source

[Launch HN: Agnost AI (YC S26) – Extract user feedback from agent conversations](https://news.ycombinator.com/item?id=48908950)
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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