Alterra.ai FAQ Bot Editor

Alterra FAQ Bot is a bot that answers questions – about your products, about your company. It converts your static FAQ files into an intelligent question answering system. It appears in a widget on your website.

Powered by an artificial neural network, the system requires a training corpus of historic user queries with the correct answers assigned to them.

FAQ Bot Editor helps you to

  • - Compose and edit your FAQ file or
  • - Upload your existing FAQ file and
  • - Upload and label your training corpus

Though FAQ Bot may appear similar to FAQ search, technically, it is not a search engine. It goes beyond searching for keywords in the FAQ files. It finds the right answer by analyzing historic user questions. Thus, you may keep the answers short and to the point. The bot will still find them, even if there is no keyword match.

On the other hand, it’s important to upload as many historic user queries to the system as possible. The bigger the training corpus, the higher the search quality.

FAQ Bot keeps a log of user queries it receives. With FAQ Bot Editor you may peruse these queries, label them and add them to the training corpus.

After finishing editing and labeling, click on the Train link – the Machine Learning system will ingest the newly added training data, and re-learn.

FAQ Bot Editor has three tabs:

  • - Edit: edit the FAQ file
  • - Label: label historic queries
  • - Settings

Edit tab

Use the Edit tab to compose and/or edit your FAQ file.

You may compose your FAQ file right in the Edit tab or copy-paste from your existing FAQ file line-by-line. You can also upload the entire existing file. You may then edit it in the Edit tab.

A typical FAQ file is a set of one question – one answer pairs.

However, users ask questions in their own words. Many of these questions are logically equivalent and shall be answered by the same answer. They are all paraphrases of one canonical question. That is, in real life, it shall be many equivalent questions – one answer. These paraphrase questions are stored in the system and used for training AI.

You may enter these paraphrase questions in the Edit tab. Click on the down arrow next to QUESTION and start typing.

These paraphrase questions may be actual questions asked by actual users. You may copy-paste them from your logs or other sources. Alternatively, you may think them up. Think how you could rephrase the canonical question.

You may include rich text (HTML) in your answers. Copy-paste it from an HTML or rich-text file. The bot will display the rich text in the web widget.

Note that the main question in the question-answer pair is not indexed. Only the paraphrase questions in the drop-down list are indexed. However, when you enter a new canonical question it is automatically copied to the paraphrase list. If you want it indexed, keep it there. Otherwise, delete it.

This way, you may make the canonical questions short and easy for you to remember. It would become more of a FAQ article title. However, if it is not a legitimate frequently asked question you may exclude it from indexing. This way, it will not skew the search algorithms.

Label tab

Use the Label tab to label historic user queries.

FAQ Bot keeps a log of user queries it receives. You may peruse and label these queries in the Label tab.

Select one query in the Query Log column on the left. The search engine will display the answers it thinks are the correct answers, ranked by relevancy. They may be correct or not. Select the correct result. It will be forever recorded. The system will learn the correct answer from you and won’t make the mistake again.

Even if the first search result is the correct one, still select it, to confirm. The AI learns from your feedback.

In addition to labeling actual user queries, you may come up with queries of your own, and label them, too. Just type your query in the ADD NEW QUERY box, and then select the correct answer.

After finishing labeling, click on the Train link – the Machine Learning system will ingest the newly added training data, and re-learn.

With the three radio buttons on top of the query log you may select

  • All queries
  • New not yet labeled queries
  • To-do queries

Normally, you should be interested in the New queries. Those are the ones you have not seen and labeled.

“To do” is a special case. They are like flagged/starred email. You’ve seen them before but did not label them then. You left them for the future. They are still waiting to be labeled. It’s your to-do list.

Special cases

Not all user queries shall be added to the training corpus. Some shall be discarded. To process them, there is a number of pre-defined classes:

Title Description
Wrong Language The user query is not in English
Garbage Completely useless queries. Garbage. Trash. Spam. Not worth answering, ever.
Ignore Meaningful queries worth answering that however shall not be added to your FAQ file: off-topic, one-off, too ambiguous, too short, too long and complex for bot, requiring a non-public answer, etc. You may forward them to humans.
To do Meaningful on-topic questions that don't have answers in the current FAQ file, but should. You may create new articles and then re-assign these queries to them. Thus, the "to-do" name.
Greeting Enables the bot to initiate a conversation with the user. The bot will send the text of the Answer of this article to the user, unprompted

The bot supports only English. It includes a language identification algorithm. When it detects a non-English query it returns the respective answer. (You can edit it.)

Currently, there are no classifiers for "Garbage", "Ignore" and "To do". The bot will NOT return "No results" response. However, when your users ask such questions you should still put them in these three classes. Otherwise, if you try to label these "bad" queries as legitimate it will wreak havoc on the algorithms. Additionally, Alterra may add the respective classifiers in the future. Your training corpus will be ready.

The pre-defined "Greeting" class is to initiate a conversation with the user. You may want your bot to be active and begin talking first, even if not prompted by the user. This pre-defined class is reserved for this purpose.

By default, the FAQ bot is pro-active. After 20 seconds, it will open the web widget and greet the user with the “Hi! We'll be happy to answer your questions” message.

If you want a different greeting, you may edit it via the Edit tab.

You may also change the timeout or disable the active pop-up altogether. This will make the bot passive: it will wait until the user asks the first question. To do that, edit the parameters in the JS code you insert in your web pages.

Pre-defined articles are editable and can be manually deleted from your corpus.

FAQ settings – Common corpora

The Settings tab allows you to combine your main FAQ corpus with pre-defined common corpora.

The latter include question – answer pairs that are not specific to your FAQ file. They are rather universal. You have an option to add these common corpora to your main FAQ.

Currently, there are two common corpora available:

chitchat

Contains dialogs that would enable the bot to answer small talk questions like "how are you?", "are you a bot or a human?", "thank you, good bye", etc. You may view this corpus by clicking on the respective json or text links.

User queries related to these topics are independent of your knowledge base. They are common across all verticals. This engine comes with a collection of such common queries. The answers, however, are under your control -- you would edit the default values as you think fit.

grab_info

This corpus covers the "contact me" user intents. Currently, it contains only one article "contact me - here is my email address". Specifically, if the user message contains the user's email address this occurrence is detected by the bot. The bot will reply with the “Thank you. We will get back with you shortly.” message. (You may edit it in the Edit tab.)

Additionally, an email notification is automatically sent to you (to the email address you provided when registering at Alterra's website.)

When a common corpus is merged, it adds a set of articles to your FAQ. Merged articles are editable.

If a common corpus is un-merged, all its articles will be removed from the main FAQ, with all its attached queries. If you had added a corpus, edited it and then un-merged it you will lose all your edits.

Misc.

You may also export your data via the Settings tab.

The tab also displays your client ID and API key (should you need one). They are automatically generated when you self-register for the service on Alterra's website.