Best practices | Tips | Troubleshooting
Chatbot are designed to respond with increasing accuracy with time. To enjoy the increasing quality of Chatbot performance, there’s a need for continuous control and supervision. Main feature of the knowledge base is that it’s flexible – every element can (and sometimes should) be changed at any given moment – it can be deleted, expanded, etc. It implies that one has to know some general rules for editing and servicing Q&A Knowledge Bases.
- Before one add a question – make sure that no such question is already in the base,
- Make sure than none of the already existing questions is synonymous to the one, one is about to add,
- If one wants to create questions – use simple forms of the words in order to fully utilize prefixes and synonyms mechanics,
- Each question should be associated with the answer,
- Remember to flag one question if it is to be shown either on the auto-complete list or in related subjects,
- Watch out for typos!
- If one wants to add an answer that goes beyond the range of current base – create a new label,
- New labels should create a logical entity with existing ones,
- Create answers that are simple, understandable and concrete,
- Respond with a complete sentence so it is clear what is the subject of the answer,
- Each answer should be associated with at least question,
- Use links in order to direct the user to chosen subpages of the website, on which one can find more information about the given subject,
- Longer responses should be divided into paragraphs with <br/>,
- Watch out for typos!
One should remember that expanding a knowledge base in never a single action, but a sequence of steps.
Maintaining knowledge base is usually much harder than creating them from the scratch – especially if handled by a person that didn’t create a specific database. First of all one should not add new elements blindly. One should have a vast knowledge of a subject of the knowledge base and a general idea concerning prefixes, synonyms and correction dictionaries. Those elements make the base much more efficient and flexible, but also intensify its complexity.
If a question is recognized wrong (or even not recognized at all), the Chatbot will provide a wrong answer or does not respond at all. There are few ways to cope with those situations. First of all, one need to figure out the nature of the problem: a) Chatbot provides a „don't know” response; b) Chatbot provides other response than it should; c) Chatbot does not provide any response or it takes too much time;
Chatbot provides a ‘don’t know’ type of response if the system does not recognize the question and cannot be associated with information contained in the currently connected databases. In such case one should always check whether the spelling of the processed question is correct. If the reason for the mistake is the spelling one should input that case into the correction dictionary. If the question was written and formed correctly, another step would be to analyze synonyms and prefixes. It is possible that the vocabulary used in the question is not currently included in existing groups of synonyms and prefixes. If the process of expending dictionaries that are mentioned above does not solve the problem one should add the analyzed question to the ‘question-answer’ association in the suitable area of the knowledge base
Chatbot provides a different answer than it should when the system does not properly recognize the question, but it still tries to provide the right answer from current databases. The easiest way to cope with such a situation is to connect a troubling question to the already existing group of ‘question-answer’ associations. Of course, one can also create a new group and add the question to that group.
If the Chatbot does not respond to the question or it takes too much time there probably exists a loop in the base or dictionaries. Beware of any duplicates, because they create a situation in which a system has few options with the same level of association and can have a problem with outputting the right answer.
Content unit, Knowledge Base unit - group of ‘question-label-answer’ association is the lowest level of the Chatbot’s knowledge hierarchy. Whole system tries to match what the user typed in with a question that is already in the base. If the question is recognized, one of the answers from the spider association will be displayed and ready by the Chatbot.
Developing answers (responses) - the process of editing responses displayed by the system. Label trees in the world of Q&A Knowledge Bases are the equivalent of sitemaps. For each label we define a minimum amount of answer.
Conversation scenario (flow, dialog) - designing connections between answers located in the Brand’s Knowledge Base. Those connections influence the list of subjects displayed along with the currently displayed response. They also allow one to prioritize the result of NLP search.
Personality / Elementary (Small - talk) - knowledge Base This knowledge base contains all the answers connected with questions concerning Chatbot | Virtual Chatbot’s personality. Also referred to as Personality, Ego.
Brand’s Knowledge Base - knowledge base that contains answers for all the questions concerning content provided by the client.
Customizing Behavioral (Chit-chat) - knowledge Base It is the process of overwriting basic responses located in the Elementary Knowledge Base – concerning Chatbot | Virtual Chatbot identification data. For example: “What’s your name?” -> Standard: “My name is Chatbot | Virtual Chatbot” -> Customized answer: “My name is Kate.”
Conversation system - conversation is one conducted ‘system-user’ conversation. The depth of the conversation stands for the number of questions asked by the user and the amount of answer provided by the system.