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.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
22:43 MIN
Using AI for a conversational developer experience
Platform Engineering untold truths: is just an infrastructure matter?
00:17 MIN
Building a custom voice AI with WebRTC and Google APIs
Raise your voice!
18:51 MIN
Developing the bot's technical and conversational framework
Design as an algorithm, not as a feature
25:55 MIN
Building real-time conversational agents
Prompt API & WebNN: The AI Revolution Right in Your Browser
15:53 MIN
The shift from developer experience to AI experience
Postgres in the Age of AI (and Devin)
05:29 MIN
Introducing conversational AI with Fabric Data Agents
Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents
20:07 MIN
The current era of AI-assisted development
From Punch Cards to AI-assisted Development
18:11 MIN
Leveraging AI as a new user experience paradigm
You are not an AI developer
Featured Partners
Related Videos
30:59Raise your voice!
Lee Boonstra
48:27Integrate your Cognitive Assistant with 3rd-party DBs and software
Felix Augenstein
56:33OpenAI for FinTech: Building a Stock Market Advisor Chatbot
Akmal Chaudhri
31:39Livecoding with AI
Rainer Stropek
26:54Make it simple, using generative AI to accelerate learning
Duan Lightfoot
28:37Speak, Code, Deploy: Transforming Developer Experience with Voice Commands
Sami Ekblad
42:38Innovating Developer Tools with AI: Insights from GitHub Next
Krzystof Czieslak
32:26Bringing the power of AI to your application.
Krzysztof Cieślak
Related Articles
View all articles
From learning to earning
Jobs that call for the skills explored in this talk.
Conversational AI und Cloud Spezialist (m/w/d)
Commerz Direktservice GmbH
Duisburg, Germany
Intermediate
Senior
NodeJS Software Engineer - Conversational AI
MANGO
Palau-solità i Plegamans, Spain
Redis
Node.js
TypeScript
Kubernetes
Natural Language Processing
AI Agent Engineer (Machine Learning Engineer)
Zendesk
Berlin, Germany
Remote
FastAPI
Machine Learning
Natural Language Processing
{"@context":"https://schema.org/","@type":"JobPosting","title":"Full Stack AI Developer
Openkit
€35-45K
PostgreSQL
TypeScript