# Keyword Training

Please read the introduction in order to understand how FAQ works.

### Introduction

Keyword training feature allows you to configure keywords for your playbooks which will trigger the corresponding playbook when the keywords are put in the input box by the user.

For example - Let's assume you have made a playbook where the chatbot flow is giving out information about the pricing to the user. You can train that playbook with keywords such as, "pricing", "price", "cost", "costing". If any user types in a query which contains any of these words, then the **Pricing** playbook will get triggered and the configured conversation will get started with the user.

**Note** - Keyword training will have higher priority if the typed in query is recognized through both FAQ training and Keyword training.

### Steps to configure Keywords

* Go to AI builder Section after logging in to the platform
* Click on the Playbook you want to train the keywords for
* Go to the **Configure** tab
* Under the **Keyword based triggers** section, you can put in the required keywords.
* After typing in the first keyword, press **Enter** before adding another keyword.

Here's a screenshot of how it will look after you've added your keywords

![Sample Keyword training for "pricing" playbook](/files/-MY4Aw5zHHF-Andnw5_j)

You can click on the cross **(X)** to remove any keyword.

**Note** - A single keyword can only be trained in a single playbook, i.e. you cannot put the same keyword in multiple playbooks. 

You can see below the pricing playbook being triggered when the keyword "pricing" is detected by the chatbot

![](/files/-MY4DdP5V65ijMx_495B)


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.intelliticks.com/ai-builder/keyword-training.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.