No doubt you’ve heard of LangChain and perhaps even played with it, seeing how easy it is to create your own LLM-powered chatbot. If you haven’t, here’s a brief intro.
LangChain was launched in 2022 as a groundbreaking tool leveraging the power of large language models to create chatbots. One of the most remarkable features of LangChain-powered chatbots is how easily they understand complex queries and provide relevant suggestions. It gives you the ability to have a context-aware chatbot on your website, using the information from your website, in a matter of hours. A quick glance at LangChain’s website shows just how easy it is to get started.
Note that LangChain does a lot more than chatbots, but for the purpose of this post, I’m going to focus on the chatbot side of things.
On the face of it, LangChain is a game-changer in the realm of website interaction, offering unparalleled capabilities for creating personalized experiences tailored to users’ specific needs and preferences. Whether you’re a travel enthusiast planning your next adventure or a sports fan seeking the latest updates, LangChain-powered chatbots are poised to revolutionize the way your customers engage with your content. With their ability to provide personalized recommendations, monetize through affiliate links, and employ efficient search mechanisms, these chatbots are set to become indispensable tools for website owners looking to enhance user experience and drive revenue.
Sounds like a dream, right? You can go ahead and fire all those customer service reps and move to a fully automated chatbot-powered sales experience.
But what if your chatbot starts to hallucinate? How many opportunities will a user give your bot after getting things wrong?
Hallucinations could cause the chatbot to misinterpret user input, leading to inappropriate or irrelevant responses. For example, if the chatbot hallucinates certain keywords or phrases in the user’s message, it may provide recommendations that do not align with the user’s actual intent or preferences.
Worse, hallucinations could introduce erroneous information into your chatbot’s recommendation generation process. This could result in the chatbot suggesting products that do not exist or are irrelevant or things you don’t even sell. The list of potential hallucination disasters goes on.
So the question is: will we reach peak chatbot and users won’t bother with them anymore? Ask yourself how often you just click the automated assistant banner away when it pops up. Are they just another version of Clippy? And are tools like LangChain just making them ubiquitous to the point of madness?
If you answered yes to any of these, it turns out you’re wrong. Users do use chatbots and often even prefer them to navigating around a website. It seems the responsibility of site owners then is to start (or continue) to provide chatbots to users but make sure that they remain relevant and useful.
From a technical point of view, this means narrowing the search context (i.e., the information that ends up in the LLM) and regularly testing your chatbot to check that it’s answering questions correctly. We’ve recently been asked to work on an analytics platform for a chatbot; that is a platform to check the most frequently asked questions, evaluate the responses, and improve the chatbot as required.
Perhaps the best way to think of chatbots is just as an extension of search. Once you start seeing them as search tools for your website (even Google competitors), their use becomes much more obvious. So while LangChain may well have made things easier, it has also made the chatbot more and more necessary.
Contact us if you want help with deploying an effective chatbot on your website.