What it does, and what it will not do

What it is good at

• Context. It has read what you told it before, so you are not starting from scratch every time you are upset.
• Untangling. Separating what happened from what you took it to mean — the two get fused when you are hurt, and pulling them apart is most of the work.
• Finding the words. Turning “you never listen” into something specific enough to respond to.
• Slowing things down. One question at a time, especially when things are escalating.
• Noticing repetition. The same argument in different clothes, three weeks apart.
• Preparing. Rehearsing a conversation you have been avoiding.

What it will not do

• Tell you that you are right. That is not a service, it is flattery.
• Diagnose anyone. Not you, and definitely not someone it has never heard from.
• Tell you what they are thinking. It does not know, and guessing does real damage.
• Promise an outcome. No software can tell you whether to stay.
• Replace a therapist. If you need clinical support, we will say so.
• Carry a secret into a shared room. Your private notes are not loaded into a joint session at all.

Where it does take a side

Neutrality is the default, and it has a hard limit. OnBetterTerms will not treat violence, threats, sexual coercion, stalking, or controlling someone's money, movements or contacts as one side of a reasonable disagreement. It will not suggest compromise or patience as a response to being harmed. In those situations your safety stops being one factor among several.

You are in charge of how it talks to you

Rate any response — helpful, not helpful, or with a reason like “too harsh”, “too long”, or “misunderstood me”. It adjusts: how direct, how detailed, how many questions, whether you want examples or a plan or just to be heard first. That preference is private. The other person is never told how you like to be spoken to.

Honesty about the model

Responses are generated by a large language model. It can be wrong, and it can be confidently wrong. Longitudinal observations are correlations in your own self-reported data, not causes, and we word them that way deliberately. Where an observation is based on thin data, it says so instead of reaching.