![]() ![]() This is certainly streamlines the design process and is a huge leap forward in sourcing and creating conversational interfaces using actual customer data. Well, that’s what Amazon has just released with Amazon Lex Automated Chatbot Designer.Įssentially, what it does, is it integrates with a bunch of Amazon Connect services (such as phone calls and Facebook Messenger), pulls out chat and call transcripts into Lex, clusters customer utterances together and suggests all of the intents and entities your bot should need. Imagine if there was a way to simply just ingest a bunch of transcripts and have you intents and training data automatically created. Instead of manually reviewing hundreds of chat transcripts and pulling out utterances to populate NLU training. Imagine if you could streamline this research process. Introducing Amazon Lex Automated Chatbot Designer It takes a lot of effort for both conversation designers and AI engineers. Failing that, you’ll shadow call centre agents, review emails, pour over the website search data, conduct customer interviews. You’ll have call recordings, live chat transcripts. If you’re lucky, you’ll have a place to start. You need to research what your users are likely to say to the bot and, crucially, how they say it. Usually, to create a language model for a specific use case like this, you need to conduct a lot of research and perform a lot of testing. Or putting together an insurance quote? Checking to see if a prescription is ready for collection? Asking about pricing and stock availability or delivery times for a specific product? All of this requires specific domain knowledge. What happens when you’re creating a ‘freeze my bank card’ use case. Microsoft have some decent templates for things like meeting room booking assistants, for example.īut these are all general use cases that don’t rely on specific industry or product-related use cases. Some platforms also have templates that’ll structure a conversation for you as a starter for ten. Google is fairly good in this department. For example, most platforms are tuned to pick out things like addresses, numbers, dates, names and other common pieces of information. Now, you can often get away with limited training data if you’re using an AI platform with ‘pre-built’ intents or entities. Without real data from real people, your chat or voice app simply won’t understand people as well as it should. Researching how your customers speak, as it relates to your use case, and including that language in your NLU training data, is imperative. Has Amazon’s latest Lex feature: Automated Chatbot Builder, just solved conversation design for everyone?įor anyone who’s ever designed any conversational interface, ever, you’ll know that you live and die by your training data.
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