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Monitor operational metrics for your Amazon Lex chatbot

Chatbots are increasingly becoming an important channel for companies to interact with their customers, employees, and partners. Amazon Lex allows you to build conversational interfaces into any application using voice and text. Amazon Lex V2 console and APIs make it easier to build, deploy, and manage bots so that you can expedite building virtual agents, conversational…



Chatbots are increasingly becoming an important channel for companies to interact with their customers, employees, and partners. Amazon Lex allows you to build conversational interfaces into any application using voice and text. Amazon Lex V2 console and APIs make it easier to build, deploy, and manage bots so that you can expedite building virtual agents, conversational IVR systems, self-service chatbots, or informational bots. Designing a bot and deploying it in production is only the beginning of the journey. You want to analyze the bot’s performance over time to gather insights that can help you adapt the bot to your customers’ needs. A deeper understanding of key metrics such as trending topics, top utterances, missed utterances, conversation flow patterns, and customer sentiment help you enhance your bot to better engage with customers and improve their overall satisfaction. It then becomes crucial to have a conversational analytics dashboard to gain these insights from a single place.

In this post, we look at deploying an analytics dashboard solution for your Amazon Lex bot. The solution uses your Amazon Lex bot conversation logs to automatically generate metrics and visualizations. It creates an Amazon CloudWatch dashboard where you can track your chatbot performance, trends, and engagement insights.

Solution overview

The Amazon Lex V2 Analytics Dashboard Solution helps you monitor and visualize the performance and operational metrics of your Amazon Lex chatbot. You can use it to continuously analyze and improve the experience of end users interacting with your chatbot.

The solution includes the following features:

  • A common view of valuable chatbot insights, such as:
    • User and session activity (sentiment analysis, top-N sessions, text/speech modality)
    • Conversation statistics and aggregations (average session duration, messages per session, session heatmaps)
    • Conversation flow, trends, and history (intent path chart, intent per hour heatmaps)
    • Utterance history and performance (missed utterances, top-N utterances)
    • Slot and session attributes most frequently used values
  • Rich visualizations and widgets such as metrics charts, top-N lists, heatmaps, and utterance management
  • Serverless architecture using pay-per-use managed services that scale transparently
  • CloudWatch metrics that you can use to configure CloudWatch alarms


The solution uses the following AWS services and features:

The following diagram illustrates the solution architecture.

The source code of this solution is available in the GitHub repository.

Additional resources

There are several blog posts for Amazon Lex that also explore monitoring and analytics dashboards:

This post was inspired by the concepts in those previous posts, but the current solution has been updated to work with Amazon Lex bots created from the V2 APIs. It also adds new capabilities such as CloudWatch custom widgets.

Enable conversation logs

Before you deploy the solution for your existing Amazon Lex bot (created using the V2 APIs), you should enable conversation logs. If your bot already has conversation logs enabled, you can skip this step.

We also provide the option to deploy the solution with an accompanying bot that has conversation logs enabled and a scheduled Lambda function to generate conversation logs. This is an alternative if you just want to test drive this solution without using an existing bot or configuring conversation logs yourself.

We first create a log group.

  1. On the CloudWatch console, in the navigation pane, choose Log groups.
  2. Choose Actions, then choose Create log group.
  3. Enter a name for the log group, then choose Create log group.

Now we can enable the conversation logs.

  1. On the Amazon Lex V2 console, from the list, choose your bot.
  2. On the left menu, choose Aliases.
  3. In the list of aliases, choose the alias for which you want to configure conversation logs.
  4. In the Conversation logs section, choose Manage conversation logs.
  5. For text logs, choose Enable.
  6. Enter the CloudWatch log group name that you created.
  7. Choose Save to start logging conversations.

If necessary, Amazon Lex updates your service role with permissions to access the log group.

The following screenshot shows the resulting conversation log configuration on the Amazon Lex console.

Deploy the solution

You can easily install this solution in your AWS accounts by launching it from the AWS Serverless Application Repository. As a minimum, you provide your bot ID, bot locale ID, and the conversation log group name when you deploy the dashboard. To deploy the solution, complete the following steps:

  1. Choose Launch Stack:

You’re redirected to the create application page on the Lambda console (this is a Serverless solution!).

  1. Scroll down to the Application Settings section and enter the parameters to point the dashboard to your existing bot:
    1. BotId – The ID of an existing Amazon Lex V2 bot that is going to be used with this dashboard. To get the ID of your bot, find your bot on the Amazon Lex console and look for the ID in the Bot details section.
    2. BotLocaleId – The bot locale ID associated to the bot ID with this dashboard, which defaults to en_US. To get the locales configured for your bot, choose View languages on the same page where you found the bot ID.Each dashboard creates metrics for a specific locale ID of a Lex bot. For more details on supported languages, see Supported languages and locales.
    3. LexConversationLogGroupName – The name of an existing CloudWatch log group containing the Amazon Lex conversation logs. The bot ID and locale must be configured to use this log group for its conversation logs.

Alternatively, if you just want to test drive the dashboard, this solution can deploy a fully functional sample bot. The sample bot comes with a Lambda function that is invoked every 2 minutes to generate conversation traffic. If you want to deploy the dashboard with the sample bot instead of using an existing bot, set the ShouldDeploySampleBots parameter to true. This is a quick and easy way to test the solution.

  1. After you set the desired values in the Application settings section, scroll down to the bottom of the page and select I acknowledge that this app creates custom IAM roles, resource policies and deploys nested applications.
  2. Choose Deploy to create the dashboard.

You’re redirected to the application overview page (it may take a moment).

  1. Choose the Deployments tab to watch the deployment status.
  2. Choose View stack events to go to the AWS CloudFormation console to see the deployment details.

The stack may take around 5 minutes to create. Wait until the stack status is CREATE_COMPLETE.

  1. When the stack creation is complete, you can look for a direct link to your dashboard on the Outputs tab of the stack (the DashboardConsoleLink output variable).

You may need to wait a few minutes for data to be reflected in the dashboard.

Use the solution

The dashboard provides a single pane of glass that allows you to monitor the performance of your Amazon Lex bot. The solution is built using CloudWatch features that are intended for monitoring and operational management purposes.

The dashboard displays widgets showing bot activity over a selectable time range. You can use the widgets to visualize trends, confirm that your bot is performing as expected, and optimize your bot configuration. For general information about using CloudWatch dashboards, see Using Amazon CloudWatch dashboards.

The dashboard contains widgets with metrics about end user interactions with your bot covering statistics of sessions, messages, sentiment, and intents. These statistics are useful to monitor activity and identify engagement trends. Your bot must have sentiment analysis enabled if you want to see sentiment metrics.

Additionally, the dashboard contains metrics for missed utterances (phrases that didn’t match the configured intents or slot values). You can expand entries in the Missed Utterance History widget to look for details of the state of the bot at the point of the missed utterance so that you can fine-tune your bot configuration. For example, you can look at the session attributes, context, and session ID of a missed utterance to better understand the related application state.

You can use the dashboard to monitor session duration, messages per session, and top session contributors.

You can track conversations with a widget listing the top messages (utterances) sent to your bot and a table containing a history of messages. You can expand each message in the Message History section to look at conversation state details when the message was sent.

You can visualize utilization with heatmap widgets that aggregate sessions and intents by day or time. You can hover your pointer over blocks to see the aggregation values.

You can look at a chart containing conversation paths aggregated by sessions. The thickness of the connecting path lines is proportional to the usage. Grey path lines show forward flows and red path lines show flows returning to a previously hit intent in the same session. You can hover your pointer over the end blocks to see the aggregated counts. The conversation path chart is useful to visualize the most common paths taken by your end users and to uncover unexpected flows.

The dashboard shows tables that aggregate the top slots and session attributes values. The session attributes and slots are dynamically extracted from the conversation logs. These widgets can be configured to exclude specific session attributes and slots by modifying the parameters of the widget. These tables are useful to identify the top values provided to the bot in slots (data inputs) and to track the top custom application information kept in session attributes.

You can add missed utterances to intents of the Draft version of your bot with the Add Missed Utterances widget. For more information about bot versions, see Creating versions. This widget is optionally added to the dashboard if you set the ShouldAddWriteWidgets parameter to true when you deploy the solution.

CloudWatch features

This section describes the CloudWatch features used to create the dashboard widgets.

Custom metrics

The dashboard includes custom CloudWatch metrics that are created using metric filters that extract data from the bot conversation logs. These custom metrics track your bot activity, including number of messages and missed utterances.

The metrics are collected under a custom namespace based on the bot ID and locale ID. The namespace is named Lex/Activity// where and are the bot and locale IDs that you passed when creating the stack. To see these metrics on the CloudWatch console, navigate to the Metrics section and look for the namespace under Custom Namespaces.

Additionally, the metrics are categorized using dimensions based on bot characteristics, such as bot alias, bot version, and intent names. These dimensions are dynamically extracted from conversation logs so they automatically create metrics subcategories as your bot configuration changes over time.

You can use various CloudWatch capabilities with these custom metrics, including alarms and anomaly detection.

Contributor Insights

Similar to the custom metrics, the solution creates CloudWatch Contributor Insights rules to track the unique contributors of highly variable data such as utterances and session IDs. The widgets in the dashboard using Contributor Insights rules include Top 10 Messages and Top 10 Sessions.

The Contributor Insights rules are used to create top-N metrics and dynamically create aggregation metrics from this highly variable data. You can use these metrics to identify outliers in the number of messages sent in a session and see which utterances are the most commonly used. You can download the top-N items from these widgets as a CSV file by choosing the widget menu and choosing Export contributors.

Logs Insights

The dashboard uses the CloudWatch Logs Insights feature to query conversation logs. Various widgets in the dashboard including the Missed Utterance History and the Message History use CloudWatch Logs Insights queries to generate the tables.

The CloudWatch Logs Insights widgets allow you to inspect the details of the items returned by the queries by choosing the arrow next to the item. Additionally, the Logs Insights widgets have a link that can take you to the CloudWatch Logs Insights console to edit and run the query used to generate the results. You can access this link by choosing the widget menu and choosing View in CloudWatch Logs Insights. The CloudWatch Logs Insights console also allows you to export the result of the query as a CSV file by choosing Export results.

Custom widgets

The dashboard includes widgets that are rendered using the custom widgets feature. These widgets are powered by Lambda functions using Python or JavaScript code. The functions use the D3.js (JavaScript) or pandas (Python) libraries to render rich visualizations and perform complex data aggregations.

The Lambda functions query your bot conversation logs using the CloudWatch Logs Insights API. The functions then use code to aggregate the data and output the HTML that is displayed in the dashboard. It obtains bot configuration details (such as intents and utterances) using the Amazon Lex V2 APIs or dynamically extracts it from the query results (such as slots and session attributes). The dashboard uses custom widgets for the following widgets: heatmaps, conversation path, add utterances management form, and top-N slot/session attribute tables.


The Amazon Lex V2 Analytics Dashboard Solution is based on CloudWatch features. See Amazon CloudWatch pricing for cost details.

Clean up

To clean up your resources, you can delete the CloudFormation stack. This permanently removes the dashboard and metrics from your account. For more information, see Deleting a stack on the AWS CloudFormation console.


In this post, we showed you a solution that provides insights on how your users interact with your Amazon Lex chatbot. The solution uses CloudWatch features to create metrics and visualizations that you can use to improve the user experience of your bot users.

The Amazon Lex V2 Analytics Dashboard Solution is provided as open source—use it as a starting point for your own solution, and help us make it better by contributing back fixes and features. For expert assistance, AWS Professional Services and other AWS Partners are here to help.

We’d love to hear from you. Let us know what you think in the comments section, or use the issues forum in the GitHub repository.

About the Author

Oliver Atoa is a Principal Solutions Architect in the AWS Language AI Services team.


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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…




This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia


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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…




A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.


For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!


If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.


In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.


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Enhance the caller experience with hints in Amazon Lex

We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up…




We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up such clues or hints based on their domain knowledge and access to business intelligence dashboards. Amazon Lex now supports a hints capability to enhance the recognition of relevant phrases in a conversation. You can programmatically provide phrases as hints during a live interaction to influence the transcription of spoken input. Better recognition drives efficient conversations, reduces agent handling time, and ultimately increases customer satisfaction.

In this post, we review the runtime hints capability and use it to implement verification of callers based on their mother’s maiden name.

Overview of the runtime hints capability

You can provide a list of phrases or words to help your bot with the transcription of speech input. You can use these hints with built-in slot types such as first and last names, street names, city, state, and country. You can also configure these for your custom slot types.

You can use the capability to transcribe names that may be difficult to pronounce or understand. For example, in the following sample conversation, we use it to transcribe the name “Loreck.”

Conversation 1

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 1111 2222 3333 4444

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Loreck

IVR: Thank you. The balance on your checking account is 123 dollars.

Words provided as hints are preferred over other similar words. For example, in the second sample conversation, the runtime hint (“Smythe”) is selected over a more common transcription (“Smith”).

Conversation 2

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Smythe

IVR: Thank you. The balance on your checking account is 456 dollars.

If the name doesn’t match the runtime hint, you can fail the verification and route the call to an agent.

Conversation 3

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Savings

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Jane

IVR: There is an issue with your account. For support, you will be forwarded to an agent.

Solution overview

Let’s review the overall architecture for the solution (see the following diagram):

  • We use an Amazon Lex bot integrated with an Amazon Connect contact flow to deliver the conversational experience.
  • We use a dialog codehook in the Amazon Lex bot to invoke an AWS Lambda function that provides the runtime hint at the previous turn of the conversation.
  • For the purposes of this post, the mother’s maiden name data used for authentication is stored in an Amazon DynamoDB table.
  • After the caller is authenticated, the control is passed to the bot to perform transactions (for example, check balance)

In addition to the Lambda function, you can also send runtime hints to Amazon Lex V2 using the PutSession, RecognizeText, RecognizeUtterance, or StartConversation operations. The runtime hints can be set at any point in the conversation and are persisted at every turn until cleared.

Deploy the sample Amazon Lex bot

To create the sample bot and configure the runtime phrase hints, perform the following steps. This creates an Amazon Lex bot called BankingBot, and one slot type (accountNumber).

  1. Download the Amazon Lex bot.
  2. On the Amazon Lex console, choose Actions, Import.
  3. Choose the file that you downloaded, and choose Import.
  4. Choose the bot BankingBot on the Amazon Lex console.
  5. Choose the language English (GB).
  6. Choose Build.
  7. Download the supporting Lambda code.
  8. On the Lambda console, create a new function and select Author from scratch.
  9. For Function name, enter BankingBotEnglish.
  10. For Runtime, choose Python 3.8.
  11. Choose Create function.
  12. In the Code source section, open and delete the existing code.
  13. Download the function code and open it in a text editor.
  14. Copy the code and enter it into the empty function code field.
  15. Choose deploy.
  16. On the Amazon Lex console, select the bot BankingBot.
  17. Choose Deployment and then Aliases, then choose the alias TestBotAlias.
  18. On the Aliases page, choose Languages and choose English (GB).
  19. For Source, select the bot BankingBotEnglish.
  20. For Lambda version or alias, enter $LATEST.
  21. On the DynamoDB console, choose Create table.
  22. Provide the name as customerDatabase.
  23. Provide the partition key as accountNumber.
  24. Add an item with accountNumber: “1111222233334444” and mothersMaidenName “Loreck”.
  25. Add item with accountNumber: “5555666677778888” and mothersMaidenName “Smythe”.
  26. Make sure the Lambda function has permissions to read from the DynamoDB table customerDatabase.
  27. On the Amazon Connect console, choose Contact flows.
  28. In the Amazon Lex section, select your Amazon Lex bot and make it available for use in the Amazon Connect contact flow.
  29. Download the contact flow to integrate with the Amazon Lex bot.
  30. Choose the contact flow to load it into the application.
  31. Make sure the right bot is configured in the “Get Customer Input” block.
  32. Choose a queue in the “Set working queue” block.
  33. Add a phone number to the contact flow.
  34. Test the IVR flow by calling in to the phone number.

Test the solution

You can now call in to the Amazon Connect phone number and interact with the bot.


Runtime hints allow you to influence the transcription of words or phrases dynamically in the conversation. You can use business logic to identify the hints as the conversation evolves. Better recognition of the user input allows you to deliver an enhanced experience. You can configure runtime hints via the Lex V2 SDK. The capability is available in all AWS Regions where Amazon Lex operates in the English (Australia), English (UK), and English (US) locales.

To learn more, refer to runtime hints.

About the Authors

Kai Loreck is a professional services Amazon Connect consultant. He works on designing and implementing scalable customer experience solutions. In his spare time, he can be found playing sports, snowboarding, or hiking in the mountains.

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

Sravan Bodapati is an Applied Science Manager at AWS Lex. He focuses on building cutting edge Artificial Intelligence and Machine Learning solutions for AWS customers in ASR and NLP space. In his spare time, he enjoys hiking, learning economics, watching TV shows and spending time with his family.


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