# 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/1.8.2/user-guide/building-assistants/respond-with-memoization-policy) with the MemoizationPolicy
> - [Handling FAQs](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/building-assistants/faqs-response-selector) using the ResponseSelector

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.

### 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 (you can remove `TEDPolicy` for now):

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

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

```markdown
## greet
* greet
  - utter_greet

## thank
* thank
  - utter_noworries

## goodbye
* bye
  - utter_bye
```

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

```markdown
## 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:

```
rasa train
rasa shell
```

This bot should now be able to reply to the intents we defined consistently, and in any order.

For example:

While it’s good to test the bot interactively, we should also add end-to-end test cases that can later be included as part of our CI/CD system. [End-to-end stories](/content/docs/rasa/user-guide/evaluating-models/#end-to-end-evaluation/index.html) include NLU data, so that both components of Rasa can be tested. Create a file called `test_stories.md` in the root directory with some test cases:

```markdown
## greet + goodbye
* greet: Hi!
  - utter_greet
* bye: Bye
  - utter_bye

## greet + thanks
* greet: Hello there
  - utter_greet
* thank: thanks a bunch
  - utter_noworries

## greet + thanks + goodbye
* greet: Hey
  - utter_greet
* thank: thank you
  - utter_noworries
* bye: bye bye
  - utter_bye
```

To test our model against the test file, run the command:

```
rasa test --e2e --stories test_stories.md
```

### Response Selectors
The [ResponseSelector](https://legacy-docs-v1.rasa.com/1.8.2/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/1.8.2/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.