Chatbot QA is Essential – Here’s How to Automate it

Generative AI is breaking swift ground in chatbot capabilities. Its remarkable ability to generate conversations, solve problems, and even produce correct lines of code has opened up a new era in human-machine communication. 

Traditional chatbots often fell short, restricted to handling basic queries, and lacking the finesse to engage in complex, two-way dialogues with users. 

Even the best bots have blindspots 

While we can celebrate chatbots’ progress in delivering more sophisticated and satisfying conversations, it is also crucial to recognize their fallibility. 

Common chatbot issues

1. Falsified truths
   Due to the nature of generative AI, chatbots cannot themselves distinguish between fact and falsehood. This means that a chatbot needs to be trained regularly.

2. Question misinterpretation
   Ambiguous or unusual questions may cause the chatbot to produce incorrect information (see above!).

3. Lack of empathy
   Customers crave compassion in certain circumstances, and chatbots cannot replicate the literal human connection.

Hence, quality assurance for chatbots becomes essential to ensure reliability, accuracy, and an organic, seamless user experience.

With any large language model, if you use it generatively, there’s always a chance for a hallucination. You can lessen the chance, but you can’t get rid of it. Essentially, such a model is a massive matrix that computes the probability of each word, then the next word, and the next. The matrix has no cognitive powers, it is simply expert at chit chat.

Mervi Sepp Rei

Data Lead, Klaus

This is where Klaus’ newest AutoQA development steps in. 

Automating chatbot QA

Our upcoming AutoQA update will:

• Distinguish between bot conversations and agent conversations (and allow you to filter between the two)
• Assess bot interactions against AutoQA categories
• Compare human and chatbot performance
• Identify necessary improvements to enhance the chatbot’s performance.

By automatically deciphering which chats involve your chatbot, Klaus segments them from the customer service agent interactions. This lets you evaluate their performance independently.

Chatbot conversations can be scored across all AutoQA categories.

This deep dive will give you valuable insights into how well the bot handles different user interactions, like providing info, answering questions, and solving issues. So you can jump in and see where they’re going right. Or take action if they’re going wrong.

The art of perfecting a chatbot flow with QA

Crafting a well-designed conversation flow means mapping out every possible user journey with the precision of a cartographer.

QA plays a pivotal role, where reviews help you stress-test the flow from every angle to uncover pitfalls, and adjust accordingly. 

To maximize customer satisfaction, you need to understand how to weigh the technological scales. How much do your customers want their customer support to be automated? Giving the chatbot too much power leads to disgruntled customers. Not giving them enough can weigh too heavily on your team of skilled customer service agents.

So perfecting a chatbot flow is a dynamic journey of development. 

QA ensures the chatbot evolves into a truly responsive and intuitive conversational partner. But if the chatbot quality assurance process adds increasing responsibilities to a QA analyst or team lead, it defeats the purpose of engaging a chatbot to reduce manual labor. 

We look forward to helping you perfect the chatbot experience!

Read more about AutoQA here.