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In today's digital age, chatbots have emerged as invaluable tools that streamline communication, enhance customer interactions, and drive business growth. However, just like any other technology, the key to unlocking their true potential lies in understanding how to properly use and train them. The introduction of customizable chatbots from AI companies such as Alta now allow any user to make their own chatbot tailored to their specific wish. However, it is important to understand how to properly set up and implement a customized chatbot.
Overall, there are 4 important steps to develop an efficient chatbot
When it comes to training a chatbot, specificity is key. Sure, chatbots have the capability of answering a wide variety of questions that people may have. However, to ensure a seamless user experience, it is important to identify precise questions and topics that the chatbot should focus on. A chatbot with an unclear purpose may prompt a frustrating user experience, as they may not get their specific questions answered properly. Defining a purpose for your chatbot will accelerate response times and leave users satisfied with prompt and accurate information.
Using Alta’s new chatbot design tool, you are able to change the suggested questions that appear first on the user’s screen. For instance, in the context of a shoe store, I can optimize the suggested questions to address common inquiries typically received by shoe stores. This approach allows users to obtain answers with a simple click of a button as opposed to typing out their inquiries.
Once the purpose of your chatbot has been established, the next course of action is to train the model with your own data. Sure, standard chatbot models already have access to information on the web. However, you want your chatbot to answer specific questions and have access to information that is not easily found on the web. Therefore, it is necessary for the chatbot to have access to data and information that it can use to create responses. By providing the chatbot with access to specific datasets, such as PDFs and documents, you empower it to have a deeper understanding of your business and its intricacies.
Going back to the Shoe Bot on Alta, attaching a terms and conditions pdf, which contains important store policies and regulations, will give the chatbot a valuable reference when answering customer questions. This will foster more accurate and reliable responses that elevates the user experience. Moreover, since the chatbot is now trained with your own data, it essentially acts as an extension of your brand’s expertise and knowledge. Therefore, when customers are satisfied with the answers of the chatbot, it establishes trust in the brand’s customer service capabilities and has the ability to attract more customers
The biggest fault surrounding chatbots is its tendency to produce long and robotic answers to questions. Sure, some may like those witty answers, but most of the time, users of a chatbot will want a simple and straightforward answer. Yes, chatbots are able to shorten previous answers if requested, but that forces the user to add in an extra line telling the chatbot to shorten the response. This can be easily prevented when designing the chatbot, as you are able to give the chatbot some initial commands and have it take on its own personality. Therefore, the most basic command could be instructing the chatbot to give simple and straightforward answers.
However, you also want to consider the tone and character that your chatbot should take on. Perhaps you want your chatbot to be funny? Perhaps you want the chatbot to answer questions as if it were an employee? Maybe you are targeting a foreign audience so you only want the chatbot to answer in a specific language? Again, these customizations are tailored to the purpose of the chatbot. For the shoe store example app, I may add in a command for the chatbot to be helpful and answer most questions in 2-3 sentences.
From the time that the first chatbot was created, it is clear that designing the perfect chatbot is a daunting task. The evidence of that is the development of new GPT models that are expected to fix some of the problems with the other models. Therefore, it is likely that your designed chatbot will not work perfectly on its first run. Testing the chatbot is a great way to find bugs and address some problems with the chatbot. However, it is also important to test prompts that are relevant to the product or service you are selling. Yes, your chatbot may not answer some general knowledge questions, but it is highly unlikely that customers are using your customized chatbot for information that they can obtain using ChatGPT or Bard. You may find some issues that stem from a lack of data or insufficient commands, which will hint you towards adding to your initial chatbot design. Therefore, designing a chatbot isn't just a one-time activity, but rather, it requires a lot of testing and has a lot of room for improvement.
In conclusion, training a chatbot is a process that requires planning and data preparation. By defining a clear purpose and scope, selecting the appropriate framework, and gathering relevant data, you can lay a strong foundation for the chatbot's success. Ultimately, with the right approach and dedication, a well-trained chatbot can be a game-changer in delivering exceptional user experiences and streamlining interactions in our increasingly digital world.
