Is my data used for model training?

The thread focused less on whether feedback can improve Claude and more on whether the consent boundary is clear enough when feedback captures a full coding conversation. Several commenters accepted the basic premise: a model provider cannot meaningfully investigate a rating without some associated context, and the interface reportedly says that submitting a report sends the current conversation for model improvement. Some also treated this as an unsurprising trade-off for consumer AI products, arguing that real usage data is more useful than benchmarks alone. But the room was sharply uneasy about Claude Code’s lightweight, recurring rating prompt. Readers worried that hitting a number merely to dismiss an interruption could be interpreted as consent to upload valuable code and prompts, especially when users signed up expecting stronger data protections. The five-year retention window and possibility of sending an entire thread, rather than only the relevant exchange, made the practice feel far broader than ordinary product feedback. Others stressed that feedback should not quietly become a blanket license to inspect proprietary work; aggregate telemetry, scoped excerpts, or feature experiments could still help diagnose quality. There was also confusion over plan boundaries: consumer Claude Code appears covered by this policy, while API and commercial offerings have different protections. The practical consensus was to disable feedback prompts or avoid them when conversations contain sensitive material, but that is viewed as an inadequate substitute for unmistakable consent.