Review the following tips to implement good conversation design practices into your Action.
Expect variations
Handle this in the "User says" input in Dialogflow. Also, use more than one intent that can map to the same action, where each intent can be triggered with different sets of "User says" phrases.
Provide helpful reprompts and fail gracefully
Sometimes your Action can't move forward because it didn't receive an input (known as a no-input) or didn't understand a user's input (known as a no-match). When this happens, Assistant first attempts to determine whether the user wants to trigger a different Action. If Assistant doesn't match the user's input to another Action, the user continues in the context of your Action. This scenario can happen at any time, so best practice is to uniquely handle no-input and no-match situations at each turn in a conversation with a fallback. Using fallbacks, you can help users get back on track.
To do this, initialize a fallbackCount
variable in your conv.data
object,
and set it to 0. Prepare an array of two fallback prompts (escalating in clarity),
and a final fallback prompt that ends the conversation.
Then, create a fallback intent (ideally one for each actionable intent in the
agent). In the intent handler, pull the fallback count from the conv.data
object, increment it, and if it is less than 3, pull the prompt from the array
of 3. If the count is at 4 or more, close the conversation using the final
prompt. In all intents that are not fallbacks, reset the fallback count to 0.
Ideally, templatize the fallbacks for specific intents to be specific to those.
Node.js
const GENERAL_FALLBACK = [ 'Sorry, what was that?' , 'I didn\'t quite get that. I can help you find good local restaurants, what do you want to know about?' , ]; const LIST_FALLBACK = [ 'Sorry, what was that?' , 'I didn\'t catch that. Could you tell me which one you prefer?' , ]; const FINAL_FALLBACK = 'I\'m sorry I\'m having trouble here. Let\'s talk again later.' ; const handleFallback = ( conv , promptFetch , callback ) = > { conv . data . fallbackCount = parseInt ( conv . data . fallbackCount , 10 ); conv . data . fallbackCount ++ ; if ( conv . data . fallbackCount > 2 ) { conv . close ( promptFetch . getFinalFallbackPrompt ()); } else { callback (); } } // Intent handlers below const generalFallback = ( conv ) = > { handleFallback = ( conv , promptFetch , () = > { conv . ask ( GENERAL_FALLBACK [ conv . data . fallbackCount ], getGeneralNoInputPrompts ()); }); } const listFallback = ( conv ) = > { handleFallback = ( conv , promptFetch , () = > { conv . ask ( LIST_FALLBACK [ conv . data . fallbackCount ], getGeneralNoInputPrompts ()); }); } const nonFallback = ( conv ) = > { conv . data . fallbackCount = 0 ; conv . ask ( 'A non-fallback message here' ); }
Java
private static final List<String> GENERAL_FALLBACK = Arrays . asList ( "Sorry, what was that?" , "I didn\'t quite get that. I can tell you all about IO, like date or location, or about the sessions. What do you want to know about?" ); private static final List<String> LIST_FALLBACK = Arrays . asList ( "Sorry, what was that?" , "I didn\'t catch that. Could you tell me which one you liked?" ); private static final List<String> FINAL_FALLBACK = Arrays . asList ( "I\'m sorry I\'m having trouble here. Maybe we should try this again later." ); @ForIntent ( "General Fallback" ) public ActionResponse generalFallback ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); int fallbackCount = ( Integer ) request . getConversationData (). get ( "fallbackCount" ); fallbackCount ++ ; request . getConversationData (). put ( "fallbackCount" , fallbackCount ); if ( fallbackCount > 2 ) { responseBuilder . add ( getRandomPromptFromList ( FINAL_FALLBACK )). endConversation (); } else { responseBuilder . add ( getRandomPromptFromList ( GENERAL_FALLBACK )); } return responseBuilder . build (); } private String getRandomPromptFromList ( List<String> prompts ) { Random rand = new Random (); int i = rand . nextInt ( prompts . size ()); return prompts . get ( i ); } @ForIntent ( "List Fallback" ) public ActionResponse listFallback ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); int fallbackCount = ( Integer ) request . getConversationData (). get ( "fallbackCount" ); fallbackCount ++ ; request . getConversationData (). put ( "fallbackCount" , fallbackCount ); if ( fallbackCount > 2 ) { responseBuilder . add ( getRandomPromptFromList ( FINAL_FALLBACK )). endConversation (); } else { responseBuilder . add ( getRandomPromptFromList ( LIST_FALLBACK )); } return responseBuilder . build (); } @ForIntent ( "Non Fallback" ) public ActionResponse nonFallback ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); request . getConversationData (). put ( "fallbackCount" , 0 ); responseBuilder . add ( "Non Fallback message" ); return responseBuilder . build (); }
Be prepared to help at any time
Create an intent that listens for help phrases like "what can I do?", "what can you tell me", or "help". In this intent, offer some (rotating) response that offers an overview of what the agent can do and directs users to a possible action. Ideally, also use follow-up help intents in Dialogflow to create different help scenarios for different actionable intents.
Node.js
const HELP_PROMPTS = [ 'There\'s a lot you might want to know about the local restaurants, and I can tell you all about it, like where it is and what kind of food they have. What do you want to know?' , 'I\'m here to help, so let me know if you need any help figuring out where or what to eat. What do you want to know?' , ]; // Intent handler const help = ( conv ) = > { reply ( conv , promptFetch . getHelpPrompt (), // fetches random entry from HELP_PROMPTS promptFetch . getGeneralNoInputPrompts ()); }
Java
private static final List<String> HELP_PROMPTS = Arrays . asList ( "There's a lot you might want to know about IO, and I can tell you all about it, like where it is and what the sessions are. What do you want to know?" , "IO can be a little overwhelming, so I\'m here to help. Let me know if you need any help figuring out the event, like when it is, or what the sessions are. What do you want to know?" ); @ForIntent ( "Help" ) public ActionResponse help ( ActionRequest request ) { return getResponseBuilder ( request ). add ( getRandomPromptFromList ( HELP_PROMPTS )). build (); }
Let users replay information
Wrap all your app.ask(output)
methods with a proxy function that adds the
output to conv.data.lastPrompt
. Create a repeat intent that listens for
prompts to repeat from the user like "what?", "say that again", or "can you
repeat that?". Create an array of repeat prefixes that can be used to
acknowledge that the user asked for something to be repeated. In the repeat
intent handler, call ask()
with a concatenated string of the repeat prefix and
the value of conv.data.lastPrompt
. Keep in mind that you'll have to shift any
SSML opening tags if used in the last prompt.
Node.js
const REPEAT_PREFIX = [ 'Sorry, I said ' , 'Let me repeat that. ' , ]; const reply = ( conv , inputPrompt , noInputPrompts ) = > { conv . data . lastPrompt = inputPrompt ; conv . data . lastNoInputPrompts = noInputPrompts ; conv . ask ( inputPrompt , noInputPrompts ); } // Intent handlers const normalIntent = ( conv ) = > { reply ( conv , 'Hey this is a question' , SOME_NO_INPUT_PROMPTS ); } const repeat = ( conv ) = > { let repeatPrefix = promptFetch . getRepeatPrefix (); // randomly chooses from REPEAT_PREFIX // Move SSML start tags over if ( conv . data . lastPrompt . startsWith ( promptFetch . getSSMLPrefix ())) { conv . data . lastPrompt = conv . data . lastPrompt . slice ( promptFetch . getSSMLPrefix (). length ); repeatPrefix = promptFetch . getSSMLPrefix () + repeatPrefix ; } conv . ask ( repeatPrefix + conv . data . lastPrompt , conv . data . lastNoInputPrompts ); }
Java
private final List<String> REPEAT_PREFIX = Arrays . asList ( "Sorry, I said " , "Let me repeat that." ); private final String SsmlPrefix = "<speak>" ; @ForIntent ( "Normal Intent" ) public ActionResponse normalIntent ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); responseBuilder . getConversationData (). put ( "lastPrompt" , "Hey this is a question" ); return responseBuilder . build (); } @ForIntent ( "repeat" ) public ActionResponse repeat ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); String repeatPrefix = getRandomPromptFromList ( REPEAT_PREFIX ); // Move SSML start tags over String lastPrompt = ( String ) responseBuilder . getConversationData (). get ( "lastPrompt" ); if ( lastPrompt . startsWith ( SsmlPrefix )) { String newLastPrompt = lastPrompt . substring ( SsmlPrefix . length ()); responseBuilder . getConversationData (). put ( "lastPrompt" , newLastPrompt ); repeatPrefix = SsmlPrefix + repeatPrefix ; } responseBuilder . add ( repeatPrefix + lastPrompt ); return responseBuilder . build (); }
Personalize the conversation with user preferences
Your Action can ask users for their preferences and remember them for later use, letting you personalize future conversations with that user.
This example Action gives users a weather report for a zip code. The following example code asks the user whether they'd like the Action to remember their zip code for later conversations.
Node.js
app . intent ( 'weather_report' , ( conv ) = > { let zip = conv . arguments . get ( 'zipcode' ); conv . data . zip = zip ; conv . ask ( getWeatherReport ( zip )); conv . ask ( new Confirmation ( `Should I remember ${ zip } for next time?` )); }); app . intent ( 'remember_zip' , ( conv , params , confirmation ) = > { if ( confirmation ) { conv . user . storage . zip = conv . data . zip ; conv . close ( 'Great! See you next time.' ); } else conv . close ( 'Ok, no problem.' ); });
Java
@ForIntent ( "weather_report" ) public ActionResponse weatherReport ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); String zip = ( String ) request . getArgument ( "location" ). getStructuredValue (). get ( "zipCode" ); responseBuilder . getConversationData (). put ( "zip" , zip ); responseBuilder . add ( getWeatherReport ( zip )); responseBuilder . add ( new Confirmation (). setConfirmationText ( "Should I remember " + zip + " for next time?" )); return responseBuilder . build (); } @ForIntent ( "remember_zip" ) public ActionResponse rememberZip ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); if ( request . getUserConfirmation ()) { responseBuilder . getUserStorage (). put ( "zip" , responseBuilder . getConversationData (). get ( "zip" )); responseBuilder . add ( "Great! See you next time." ). endConversation (); } else { responseBuilder . add ( "Ok, no problem." ). endConversation (); } return responseBuilder . build (); }
After asking the user which zip code they're in during their first dialog, you can skip that prompt during their next invocation and use the same zip code. You should still provide an escape route (like a suggestion chip allowing them to pick a different zip code) but by reducing a turn of conversation in the common case, you create a much more seamless experience.
Node.js
app . intent ( 'weather_report' , ( conv ) = > { let zip = conv . arguments . get ( 'zipcode' ); if ( zip ) { conv . close ( getWeatherReport ( zip )); } else if ( conv . user . storage . zip ) { conv . ask ( new SimpleResponse ( getWeatherReport ( conv . user . storage . zip ))); conv . ask ( new Suggestions ( 'Try another zipcode' )); } else { conv . ask ( 'What\'s your zip code?' ); } }); app . intent ( 'provide_zip_df' , ( conv ) = > { conv . user . storage . zip = conv . arguments . get ( 'zipcode' ); conv . close ( getWeatherReport ( conv . user . storage . zip )); });
Java
public ActionResponse weatherReport2 ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); String zip = ( String ) request . getArgument ( "location" ). getStructuredValue (). get ( "zipCode" ); if ( zip != null ) { responseBuilder . add ( getWeatherReport ( zip )). endConversation (); } else if (( zip = ( String ) responseBuilder . getUserStorage (). get ( "zip" )) != null ) { responseBuilder . add ( new SimpleResponse (). setTextToSpeech ( getWeatherReport ( zip ))); responseBuilder . add ( new Suggestion (). setTitle ( "Try another zipcode" )); } else { responseBuilder . add ( "What's your zip code?" ); } return responseBuilder . build (); }
Customize for returning users
Maintaining some state between conversations ensures a far more natural
experience for return users. The first step in crafting this experience is to
greet return users differently. For instance, you can taper the greeting or
surface useful information based on past conversations. To do this, use the
incoming AppRequest.User
lastSeen
property to determine whether the user
has interacted with your Action before. If the lastSeen
property is included
in the request payload, you can use a different greeting than normal.
The code below uses the Node.js client library to fetch the value of last.seen
.
Node.js
// This function is used to handle the welcome intent // In Dialogflow, the Default Welcome Intent ('input.welcome' action) // In Actions SDK, the 'actions.intent.MAIN' intent const welcome = ( conv ) = > { if ( conv . user . last . seen ) { conv . ask ( `Hey you're back...` ); } else { conv . ask ( 'Welcome to World Cities Trivia!...' ); } }
Java
// This function is used to handle the welcome intent // In Dialogflow, the Default Welcome Intent ('input.welcome' action) // In Actions SDK, the 'actions.intent.MAIN' intent public ActionResponse welcome ( ActionRequest request ) { ResponseBuilder responseBuilder = getResponseBuilder ( request ); if ( request . getUser (). getLastSeen () != null ) { responseBuilder . add ( "Hey you're back..." ); } else { responseBuilder . add ( "Welcome to Number Genie!..." ); } return responseBuilder . build (); }
You can further enhance this greeting by tailoring the response to the actual
value of lastSeen
. For instance, users whose last interaction occurred many
months before the current interaction might receive a different greeting than
those who used the Action the day before.
In-conversation volume control
On supported devices, the Assistant lets users control device volume within your conversational Action by saying things like "turn the volume up" or "set the volume to 50 percent" . If you have intents that handle similar training phrases, your intents take precedence. We recommend letting the Assistant handle these user requests unless your Action has a specific reason to.
