The quality of the responses

The accuracy of OnChat's responses depends on the quality and relevance of the training data you provide. When properly trained, it can deliver precise answers to customer inquiries in seconds. However, as an AI model, it may sometimes produce incomplete or incorrect responses, particularly when encountering unfamiliar questions. Regular monitoring and updates to the source data can help enhance its accuracy over time.

Fine-tuning your virtual assistant responses allows you to customize and optimize how your chatbot interacts with users. This process involves refining the accuracy of the responses to better align with your brand voice and meet the needs of your audience. By reviewing and adjusting the responses based on user feedback and performance analytics, you can enhance the overall user experience and ensure that your virtual assistant provides valuable and relevant information to users.

 

Quality of the data sources

The effectiveness of your chatbot's responses is heavily influenced by the quality of the data sources you supply. Onchat primarily relies on the content of your website as data sources, but it's important to note that it can only process text and cannot analyze images or videos. Additionally, some websites may not be easily accessible for scraping, which could limit the information available to Onchat.

If you find that your chatbot is unable to answer questions based on your website content, it's possible that this is the reason. You can address this issue by manually adding the relevant information as plain text through copy and paste. This ensures that your chatbot has access to the necessary data to provide accurate and helpful responses to user queries.

 

 

Analyse your chat history

You have access to all conversations generated by the chatbot and your customers. Through analysis, you can extract valuable insights on the information that needs to be added to your existing data sources. By reviewing chat histories and noting recurring topics or unanswered questions, you can compile a set of additional questions and answers. 

These can then be added as a Q&A source type, enhancing your chatbot's ability to provide more accurate and comprehensive responses. This iterative process of learning from user interactions allows you to continually refine and improve your chatbot's performance over time.

Create Url Mapping

If your chatbot isn't providing the correct URL for a specific question, create one or more text sources that map the correct URLs to their corresponding page names. This will help your chatbot better understand the URLs to different pages, categories or products.

 

 

Prevent hallucination

In the field of artificial intelligence, a hallucination refers to a response generated by AI that contains false or misleading information presented as fact. This can occur due to various factors such as insufficient training data, biases in the training data, or limitations in the AI model's understanding of context or semantics. 

Detecting hallucinations in your chatbot involves thorough testing before launching it. During this testing phase, carefully review the responses provided by the chatbot to ensure they are accurate and reliable. If you identify any instances of misleading information or hallucinations, take immediate action to correct them. This can involve adding additional data sources, refining the training data. By continuously monitoring and improving your chatbot, you can enhance its reliability and trustworthiness, ultimately providing users with a more valuable and accurate experience.

 

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