# Tutorial: Building Assistants

After following the basics of setting up an assistant in the [Rasa Tutorial](/content/docs/rasa/user-guide/rasa-tutorial/index.html), we’ll now walk through building a basic FAQ chatbot and then build a bot that can handle contextual conversations.

### Building a Simple FAQ Assistant

FAQ assistants are the simplest assistants to build and a good place to get started. These assistants allow the user to ask a simple question and get a response. We’re going to build a basic FAQ assistant using features of Rasa designed specifically for this type of assistant. In this section we’re going to cover the following topics:

> - [Responding to simple intents](https://legacy-docs-v1.rasa.com/1.9.7/user-guide/building-assistants/respond-with-memoization-policy) with the MemoizationPolicy
> - [Handling FAQs](https://legacy-docs-v1.rasa.com/1.9.7/user-guide/building-assistants/faqs-response-selector) using the ResponseSelector

We’re going to use content from [Sara](https://github.com/RasaHQ/rasa-demo), the Rasa assistant that, amongst other things, helps the user get started with the Rasa products. You should first install Rasa using the [Step-by-step Installation Guide](/content/docs/rasa/user-guide/installation/#step-by-step-installation-guide/index.html) and then follow the [Rasa Tutorial](/content/docs/rasa/user-guide/rasa-tutorial/index.html) to make sure you know the basics.

To prepare for this tutorial, we’re going to create a new directory and start a new Rasa project.

```bash
mkdir rasa-assistant
rasa init
```

Let’s remove the default content from this bot, so that the `nlu.md`, `stories.md` and `domain.yml` files are empty.

### Memoization Policy

The MemoizationPolicy remembers examples from training stories for up to a `max_history` of turns. The number of “turns” includes messages the user sent and actions the assistant performed. For the purpose of a simple, context-less FAQ bot, we only need to pay attention to the last message the user sent, and therefore we’ll set that to `1`.

You can do this by editing your `config.yml` file as follows:

```yaml
policies:
- name: MemoizationPolicy
  max_history: 1
- name: MappingPolicy
```

**Note:** The MappingPolicy is there because it handles the logic of the `/restart` intent, which allows you to clear the conversation history and start fresh.

Now that we’ve defined our policies, we can add some stories for the `goodbye`, `thank` and `greet` intents to the `stories.md` file:

```yaml
## greet
* greet
  - utter_greet

## thank
* thank
  - utter_noworries

## goodbye
* bye
  - utter_bye
```

We’ll also need to add the intents, actions and responses to our `domain.yml` file in the following sections:

```yaml
intents:
  - greet
  - bye
  - thank

responses:
  utter_noworries:
    - text: No worries!
  utter_greet:
    - text: Hi
  utter_bye:
    - text: Bye!
```

Finally, we’ll copy over some NLU data into our `nlu.md` file:

```yaml
## intent:greet
- Hi
- Hey
- Hi bot
- Hey bot
- Hello
- Good morning
- hi again
- hi folks

## intent:bye
- goodbye
- goodnight
- good bye
- good night
- see ya
- toodle-oo
- bye bye
- gotta go
- farewell

## intent:thank
- Thanks
- Thank you
- Thank you so much
- Thanks bot
- Thanks for that
- cheers
```

You can now train a first model and test the bot by running the following commands:

```bash
rasa train
rasa shell
```

### Response Selectors

The [ResponseSelector](https://legacy-docs-v1.rasa.com/1.9.7/nlu/components/#response-selector) NLU component is designed to make it easier to handle dialogue elements like [Small Talk](https://legacy-docs-v1.rasa.com/1.9.7/dialogue-elements/small-talk/#small-talk) and FAQ messages in a simple manner. By using the ResponseSelector, you only need one story to handle all FAQs, instead of adding new stories every time you want to increase your bot’s scope.

People often ask Sara different questions surrounding the Rasa products, so let’s start with three intents: `ask_channels`, `ask_languages`, and `ask_rasax`.

To handle these FAQs, we need to add a new section in our `domain.yml` file as follows:

```yaml
intents:
  - greet
  - bye
  - thank
  - faq
  - ask_channels
  - ask_languages
  - ask_rasax

responses:
  respond_faq:
    - text: This is the response from the ResponseSelector.
```

### Conclusion
Using the features we described in this tutorial, you can easily build a context-less assistant. When you’re ready to enhance your assistant with context, make sure to take into consideration everything we’ve discussed in the article.