Conversations and messages

AskAnyDocs keeps separate views for conversations and individual messages. These views help you understand what visitors ask, how the assistant responds, and where your documentation needs more detail.

Why review conversations

An AI documentation assistant improves when the source content improves. Conversation review shows the gap between what your documentation says and what users actually ask.

Use conversation history to find:

• repeated unanswered questions
• confusing onboarding topics
• missing pricing or plan details
• pages that users cannot find on their own
• support issues that should become documentation
• cases where the bot answered from outdated content

Reviewing conversations is especially useful during the first days after launch because real users will ask questions differently than your internal test team.

Conversations view

Use the conversations page to review user sessions at a higher level. This view is useful when you want to understand how questions evolve across multiple turns.

A conversation can show whether the first answer was enough or whether the visitor had to keep clarifying. Multiple follow-up questions about the same topic often mean the source material is incomplete or the answer needs a clearer structure.

Look for conversations where users:

• repeat the same question in different words
• ask for a human after a weak answer
• switch topics because the bot misunderstood intent
• ask about content that does not exist in the knowledge base

Messages view

Use the messages page when you want to inspect single questions and answers in more detail. Message-level review is helpful for auditing specific topics, checking answer phrasing, and finding patterns across many users.

For example, if several messages ask about billing limits, you may need a clearer pricing article or a dedicated FAQ section. If users repeatedly ask how to install the widget, improve the embed documentation and reindex it.

Escalation markers

If a conversation or message required human escalation, the admin UI marks it so you can identify content gaps or support handoff situations faster.

Escalations are not always bad. They are useful signals. Some questions should go to a human, especially account-specific, billing-sensitive, or legal questions. The goal is to identify which escalations can be prevented by better documentation and which should remain a support workflow.

What to look for

The most useful signals are usually:

• repeated unanswered questions
• unclear onboarding questions
• content gaps between docs and website
• escalation-heavy topics that should be documented better
• answers that quote old product names or outdated plan limits
• questions that should be routed to sales or support

Turning insights into better docs

When you find a weak answer, update the source document rather than only changing the bot settings. Add a direct answer, a short example, and a clear heading that matches the user's language. Then reindex the updated source and test the same question again.

Over time, this review loop turns real user questions into stronger support content and better AI answers.

Escalation follow-up

When a message is escalated, review both the answer quality and the integration outcome. If the conversation includes user metadata, support notifications, helpdesk tickets, CRM records, and live chat handoff can include the visitor's email, name, phone, or internal ID.

For setup details, see Integrations and escalation and Pass user data to the widget.

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