Xavier Portilla Edo
Creating bots with Dialogflow CX
#1about 2 minutes
What is Dialogflow CX and its state machine approach
Dialogflow CX uses a state machine approach with visual flows to simplify the management of complex conversational states.
#2about 2 minutes
Organizing conversations with flows and pages
Structure complex assistants using flows for high-level features and pages for individual states to create reusable conversational components.
#3about 3 minutes
How NLU works with intents and entities
Natural Language Understanding (NLU) works by matching user utterances to defined intents and extracting key parameters as entities.
#4about 1 minute
The four-step NLU design and training process
Follow the iterative lifecycle of designing, building, training, and evaluating the NLU model to improve your assistant's accuracy.
#5about 3 minutes
Understanding the end-to-end system architecture
The system architecture involves a client application calling the Dialogflow CX API, which processes the state and can trigger external webhooks.
#6about 2 minutes
Building a serverless backend on Google Cloud
A recommended backend uses Google Cloud Functions for serverless webhooks, Cloud Storage for assets, and Firebase for a persistent database.
#7about 2 minutes
Using client SDKs and the development lifecycle
Use the available client SDKs in languages like Python and JavaScript to programmatically manage agents as part of a structured development workflow.
#8about 2 minutes
Testing your agent in the console and with automated tests
Validate your assistant using the built-in test agent for manual checks and save interactions to create automated test cases for CI/CD.
#9about 3 minutes
Integrating with built-in and custom platforms
Connect your bot using plug-and-play integrations like Facebook Messenger or build a man-in-the-middle service for custom channels like Telegram.
#10about 4 minutes
A live demo of the Dialogflow CX console
This walkthrough of the user interface shows how to navigate flows, pages, routes, intents, and entity types to build a bot.
#11about 2 minutes
Managing resources with a custom CLI tool
A community-built command-line interface allows developers to perform CRUD operations and test Dialogflow CX agents from the terminal.
#12about 3 minutes
Validating NLU models with an automated profiler
The CLI's NLU profiler uses YAML files to create and run automated test suites for validating intent and entity matching.
#13about 4 minutes
Integrating generative AI for dynamic responses
Use webhooks to call large language models like Google's PaLM or Vertex AI to generate more natural and varied bot responses.
#14about 4 minutes
Comparing Dialogflow CX on advantages and security
Dialogflow CX offers an easier development experience than competitors while leveraging the security and compliance of the Google Cloud Platform.
#15about 4 minutes
Strategies for multilingual bots and migrating from Dialogflow ES
Create separate agents for languages with very different interaction patterns and use official tools to help migrate from Dialogflow ES.
#16about 10 minutes
Practical applications, integrations, and adoption strategies
Connect to external systems like CRMs via the API and justify bot development to leadership by presenting data on time saved.
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