Reliable answers

The assistant is at its sharpest in a short, focused conversation about one topic. The longer and fuller a chat becomes — many questions, many documents, several topics mixed together — the more the assistant has to hold in view at once. At some point it can start to mix up details or “fill in” a passage that isn’t literally there. This isn’t a PrudAI quirk: it applies to every current AI model and it’s measurable — the longer the conversation, the higher the chance of such mistakes.

The good news: this is largely in your own hands.

Start a new chat per topic

This is by far the most effective habit. Begin a new chat for each new topic or part of the case file. A short, focused chat keeps the assistant anchored to exactly the documents that matter for that question — and that is precisely what stops it from mixing up sources.

Rules of thumb:

New topic? New chat. Don’t carry one chat from the liability question to legal costs to the writ of summons.
Keep the number of documents per chat focused — only what’s relevant to that question, not your entire case file in one running conversation.
Notice a chat getting “tired” (answers become vaguer, or the assistant refers to something you don’t recognise)? Start again in a fresh chat. You lose nothing: your documents and projects stay put.

The product warns you itself

You do not have to judge this by feel alone: the chat tells you when a conversation is getting too full.

Warnings that escalate. As a conversation grows, the message changes with it:

“This conversation is getting long”: in long conversations, the quality of answers can gradually decline.
“This conversation is very long”: we recommend starting a new conversation.
“This conversation is extremely long”: answer quality is likely to decline noticeably, and we strongly recommend starting over.
“This conversation is nearing its limit”: older messages will soon be summarised automatically and detail may be lost.

A standing notice in the chat. Alongside those passing warnings, a card can stay in the conversation with two buttons: Start a focused conversation (it takes your dossier along) and Continue here. The card has two headings, depending on the cause: “This conversation is getting long” when it is the number of messages, and “This conversation holds a lot of material” when the length sits mostly in the retrieved documents. That second one is the reason to keep your documents per chat focused too.

Summarising. If you do not act, at some point the assistant summarises older messages itself. You then see the message “Conversation summarised”. The answer still reflects the full conversation, but detail from those older messages can be lost. That is exactly the moment you would rather have got ahead of.

So do not treat these warnings as noise. They are the signal to carry the topic you are working on into a fresh chat.

Always check the source

Treat every answer as a draft. Click the source pill or document citation inside the answer and read the source text yourself before you use anything in your advice or court document. See a reference to a document or passage you don’t recognise? Don’t trust it, start a new chat and ask again with only the relevant document attached. More on this: Citations & source availability and Chat.

This is not just our advice either: below the input field there is a fixed disclaimer stating that you are working with an AI system and should always verify important answers yourself. See Chat.

What the assistant should do

If the assistant hasn’t actually retrieved a document, it should say so — not guess at its contents. If you notice it quoting a passage that’s wrong anyway, give the message a 👎 (Could be better) with a short note under “What went wrong?”. That signal reaches us and helps us sharpen the assistant further.

What the research shows

This isn’t a PrudAI quirk but a well-documented property of every current AI model:

Long inputs become less reliable. Independent research tested 18 leading models from different providers and found performance degrades steadily as the input grows — even on simple tasks.
Information in the “middle” is used less well. Models use information at the beginning and end of a long context better than information buried in the middle.
Long conversations amplify this. Across multiple turns, accuracy drops by roughly 39% on average versus a single focused question. A key cause: when a model is missing something, it sometimes “fills in” an assumption and then anchors on it — exactly the pattern you want to avoid.

This is precisely why “a new chat per topic” works: you keep the context short, so the model never falls into that pattern.