You are reviewing content, not coaching an agent
Human quality assurance ends in a conversation with a person who can learn. AI review ends in an edit to your source material or your routing rules, which makes it both less delicate and easier to neglect — nobody feels awkward, so nobody feels urgency either. Treat it as a standing content task with an owner and a slot in the week, or it will not happen at all. The good news is that every fix is permanent and applies to every future conversation, which is a far better return than coaching the same point repeatedly.
Read the handovers first
The highest-value sample is not random: it is the conversations where AI handed over to a human, or where the visitor asked for one. Each is a labelled example of the system reaching its limit, already sorted for you. Read a batch and sort them into two piles — correct handovers, where a person genuinely should have taken over, and avoidable ones, where the answer existed in your documentation but was not found or not trusted. The second pile is your work queue.
Sort failures by cause, because the fixes differ
Most bad AI answers come from one of three causes, and confusing them wastes effort. A coverage gap means the content does not exist — write it. A retrieval failure means the content exists but the question was phrased differently — add the customer's actual wording to the article, since visitors and documentation writers rarely use the same words. A grounding failure means the system answered confidently beyond its material, which is the serious one, and points at either scope that is too broad or content too vague to constrain it. Only the first is fixed by writing more.
Watch for confident wrongness specifically
A visible failure — the AI says it does not know and passes to a human — is a working system. The dangerous case is a fluent, plausible, wrong answer, because nobody escalates it and the visitor leaves believing something untrue. These rarely appear in complaints; they surface later as a support case about a promise you never made. When reviewing, read for accuracy rather than tone, and be suspicious of the most polished answers about topics your documentation covers thinly.
Close the loop and keep the score
An improvement you cannot see is an improvement you will stop making time for. Track a small number of things over weeks: how often AI conversations end without a handover, how often visitors immediately ask for a person, and how many reviewed answers were accurate. You are looking for direction, not precision. If handovers fall while accuracy holds, the content is genuinely improving; if handovers fall while accuracy slips, you have widened the scope too far and should pull it back.
How MyLiveChat fits
MyLiveChat keeps AI and human turns in one transcript, so a review reads as a single conversation and you can see exactly where the AI stopped being useful and what the human said instead — usually the best draft of the answer your content is missing. Because the AI answers from content you supply, the fix for most failures is editing that source material, and the improvement applies to every conversation afterwards without retraining anyone.
A sampling plan you will actually keep
Review programmes fail by being too ambitious. A plan to read every AI conversation survives one busy week; a plan to read twelve a week survives a year, and the second one is worth far more.
Sample in three slices. Take all handovers first if the volume allows, since those are the failures the AI itself flagged. Add a handful of chats the AI closed without escalation, which is where silent wrongness hides — nobody complained, so nobody looked. Then take a few at random, because both other slices are biased and the random pull is what catches drift you were not looking for. Twelve to fifteen conversations a week is enough to see the patterns; more just delays the review until it stops happening.
Fix the day and the person. A recurring half-hour with a named owner outlasts any amount of enthusiasm about quality, and the review that happens is infinitely better than the thorough one that does not.
The rubric, in four questions
Keep scoring simple enough that two reviewers agree without a meeting. Four yes-or-no questions per conversation do most of the work.
- Was it accurate? Not plausible — accurate, against your actual content and policy today.
- Was it complete enough to act on? A technically true answer that leaves the visitor still needing a human is a partial failure worth counting as one.
- Did it stay in scope? Answering a question outside what it was given content for is a scope problem, even when the answer happens to be right.
- Did it hand over when it should have? Both directions count: a handover that was not needed costs agent time, and a missed one costs trust.
Record the failures by cause rather than by score, because the cause determines the fix. Missing content is an authoring job, stale content is an ownership job, out-of-scope answers are a configuration job, and a confidently wrong answer drawn from correct content is usually a phrasing problem in the source material.
Who reviews, and what it costs
The best reviewer is an experienced agent, not a manager, because they know what the right answer looks like and they recognise the conversations that would have gone badly. Rotate it if you can — a fresh reader catches things a regular reviewer has learned to overlook — and have whoever owns the source content sit in occasionally, since most of the fixes land on their desk.
Budget honestly: fifteen conversations takes roughly thirty to forty minutes to read and tag, plus an hour or so a month to make the content edits the review generates. That is a real cost, and it is far smaller than the cost of an AI that has been quietly answering a common question wrong since spring. Track one number over time — the share of sampled conversations with no failure of any kind — and expect it to improve in steps as content lands, not smoothly.
While you are reviewing answers, check the name attached to them. Setting the name and photo your AI assistant shows explains why that identity also follows the conversation into your transcripts.
The same review instinct applies to drafts written for you rather than sent for you. Letting AI draft a ticket reply puts a human between the model and the customer by design, which changes what you are reviewing for.
If you are sampling live conversations rather than reading transcripts afterwards, the AI indicator is not the signal to trust. How your console knows the AI is still handling a chat explains why it fails to a negative.