AI-assisted practical guide. Examples are hypothetical; proposed workflows are editorial suggestions.
An AI clustering pass can suggest repeated support questions, but similar wording does not always mean the same customer need. Review proposed groups before merging records or changing published help content.
Prepare a bounded sample
Use fictional or appropriately redacted messages for an initial trial. Keep stable message IDs so every suggested group can be traced back to its inputs. Remove information unnecessary to understanding the question.
Ask the assistant to explain the shared intent and identify outliers. Require it to preserve questions that depend on different products, account states, or policies.
Inspect a hypothetical cluster
Imagine two messages say “I cannot sign in.” One concerns a forgotten password; the other concerns an account that has not been activated. A single broad heading may help navigation, but treating them as identical cases could hide different resolution paths.
Read the originals in each proposed group. A convincing cluster label does not prove that every member belongs.
Use the result as a proposal
Record accepted groups, rejected merges, and unresolved cases. Keep source messages intact unless a separate authorized process governs retention. If the analysis suggests a new help article, verify its answer against actual policy and product behavior. The useful output is an editorial map of recurring needs, not an automatic instruction to erase distinctions or send the same reply to everyone.