# Build conversation models
A conversation model defines what users can say to your Actions and how your
Actions respond to users. The main building blocks of your conversation model
are [intents](https://developers.google.com/assistant/conversational/intents), [types](https://developers.google.com/assistant/conversational/types), [scenes](https://developers.google.com/assistant/conversational/scenes), and
[prompts](https://developers.google.com/assistant/conversational/prompts). After one of your Actions is invoked, Google Assistant
hands the user off to that Action, and the Action begins a conversation with the
user, based on your conversation model, which consists of:
- **Valid user requests** - To define what users can say to your Actions, you
create a collection of intents that augment the Assistant NLU, so it can
understand requests that are specific to your Actions. Each intent defines
training phrases that describe what users can say to match that intent. The
Assistant NLU expands these training phrases to include similar phrases, and
the aggregation of those phrases results in the intent's language model.
- **Action logic and responses** - Scenes process intents, carry out required logic, and generate prompts to return to the user.
 **Figure 1.** A conversation model consists of intents, types, scenes, and prompts that define your user experience. Intents that are eligible for invocation are also valid for matching in your conversations.
## Define valid user requests
To define what users can say to your Actions, you use a combination of intents
and types. User intents and types let you augment the Assistant NLU with your
own language models. System intents and types let you take advantage of built-in
language models and event detection like users wanting to quit your Action or
Assistant detecting no input at all.
### Create user intents
User intents let you define your own training phrases that define what users
might say to your Actions. The Assistant NLU uses these phrases to train itself
to understand what your users say. When users say something that matches a
user intent's language model, Assistant matches the intent and notifies
your Action, so you can carry out logic and respond back to users.
### Create system intents
System intents let you take advantage of intents with pre-defined language
models for common events like users wanting to quit your Action or when user
input times out. To create system intents:
### Create custom types
Custom types let you create your own type specification to train the NLU to
understand a set of values that should map to a single key.
To create a custom type:
## Build Action logic and responses
The Assistant NLU matches user requests to intents, so that your Action can
process them in scenes. Scenes are powerful logic executors that let you
process events during a conversation.
### Create a scene
The following sections describe how to create scenes and define functionality
for each scene's lifecycle stage.
To create a scene:
### Define one-time setup
When a scene first becomes active, you can carry out one time tasks in the
**On enter** stage. The On enter stage executes only once, and is the only
stage that doesn't run inside a scene's execution loop.
### Check conditions
Conditions let you check slot filling, session storage, user storage, and
home storage parameters to control scene execution flow.
### Define slot filling
Slots let you extract typed parameters from user input.
#### Slot value mapping
In many cases, a previous intent match can include parameters that partially or
entirely fill a corresponding scene's slot values. In these cases, all slots
filled by intent parameters map to the scene's slot filling if the slot name
matches the intent parameter name.
For example, if a user matches an intent to order a beverage by saying *"I want
to order a large vanilla coffee"*, existing slots for size, flavor, and beverage
type are considered filled in the corresponding scene if that scene defines same
slots.
> [!NOTE]
> **Note:** Intent data is stored for one conversation turn and is overwritten with the next user input. If you need this data to persist, you can store this data in [session storage](https://developers.google.com/assistant/conversational/storage).
### Process input
During this stage, you can have the Assistant NLU match user input to intents.
You can scope intent matching to a specific scene by adding the desired intents
to the scene. This lets you control conversation flow by telling Assistant
to match specific intents when specific scenes are active.