ActiveChat is a powerful AI platform with natural language processing capabilities that can help customer service, online shopping, and marketing chatbots understand the conversations they have with customers. Through partnering with OpenAI team, Activechat has created something extraordinary: all you need to do is describe in plain language the context of your business and provide some sample questions and answers that your human agents might come across during their daily interactions with clients, then GPT-3 will generate instant hints for them so they can answer almost any question a customer may have. To illustrate this concept we’ll use an example of a bike shop assistant. The default settings in Activechat’s GPT-3 integration are used here – the context being conversations between website visitors and virtual assistants on pages where bicycles are sold; these assistants should be able to respond to queries about different bike brands or types as well as helping customers choose one based on what they’ve described. As an initial seed for the artificial intelligence system we provided examples such as “What’s the difference between a carbon and regular bike?” (Answer: Carbon frames tend to be lighter than aluminum – up to one pound for mountain bikes or half a pound for road ones) or “How much does a good bike cost?” (Answer: Road bikes range from $350-$700; Mountain bikes around $1000; Single-speed bikes—$400; Beach cruisers—$200–300; Recumbent bicycles—$1000–2000; Kids’ bikes—between $140 and $200). We also included instructions like “how do I service my bike?” (Answer: Regularly servicing your bicycle ensures it runs efficiently without worn components. The more often you ride it, especially if you go off-road or ride in wet conditions, the more frequently it needs maintenance). Once these steps are taken GPT-3 offers several relevant answers which appear when hovering over each prompt icon – clicking any of them copies the response into message window allowing agents either send it immediately or edit it by adding links to product pages etc., this reduces time spent finding and typing responses by 70%-80%. If none of these options seem satisfactory simply click refresh icon for new suggestions powered by all knowledge available online including Common Crawl data Wikipedia articles plus books!