Human support requires human queries

‘Todays fish is trout a la creme’. (Red Dwarf, 1988, Balance of Power).

We are committed to providing support delivered by real humans. We know how frustrating it can be to have to fight your way past chatbots and virtual assistants that can’t help you in order to get to a human that can.

In fact, when you contact us, your message will be dealt with not only by a human, but by one of our technical staff who, on other days of the week, will be managing our routers, running our DNS servers, developing our control panel and maintaining all our other infrastructure.

LLMs break processes designed for humans

Modern Large Language Models (LLMs) are very impressive, and we know that many people find them useful in spite of their inherent and well-documented unreliability. One of the downsides of LLMs is their ability to produce large quantities of text that can overwhelm processes that are built to deal with human input. This is being seen in many places, from bug bounty programmes being DoSed by LLM-generated submissions, to schools unable to cope with a huge increase in the volume and size of complaints from parents.

We’ve now started to see this in our support queries. Fortunately, the volume is not yet problematic, but we have had cases where we’ve spent a disproportionate amount of time dealing with LLM-assisted ticket submissions. This includes time spent trying to decipher what the question actually is thanks to LLM-induced obfuscation, and also time spent answering tickets that are just much longer than they would have been had they been written by a human.

Dealing with LLM-generated support queries

Our target is to respond to all support queries within one working day. Providing a 100% human support response on this basis simply doesn’t work if support requests are being generated automatically. Therefore, we have amended this target to exclude queries that we believe have been generated by an LLM; if you submit a support request which we believe has been made harder or more time consuming to answer by the use of an LLM, we may reject it and ask you to resubmit a human-authored request instead.

If you want to use an LLM to help solve your query yourself that’s great. But if that’s unsuccessful, please don’t send us your LLM output as a support query; send us your input – your prompt – instead.