Generative Dialogflow CX vs GPT chatbot

Recently, Dialogflow added Generative AI agents to Dialogflow CX.

In my view, Generative Dialogflow CX is a much better option for website chatbots than using the GPT API, especially if you are not a programmer.

Here are some reasons:

Much easier for non-programmers to design conversation flows

If you are building a multi-turn conversational chatbot, you cannot visually design the conversation flow with the GPT API. On the other hand, Dialogflow CX evolved from Dialogflow ES primarily to allow non-programmers to design conversation flows.

This means it is much easier for non-programmers to design conversation flows using Dialogflow CX since it includes an actual flow builder user interface.


I would in fact recommend non-programmers try using Dialogflow ES for their chatbot first. It has a very generous free tier, which means you can experiment with it quite a bit on a free plan. You can use Mindomo as your flowchart builder for ES, and then migrate your bot to CX only if you need to.

If you use the explicative approach for building your ES bot, migrating to CX should be fairly easy. In some cases, you might even realize that your bot is not complex enough to require Dialogflow CX, in which case you can just remain on the free tier.

Out-of-the-box website chat widget

While there are some companies which provide a way to integrate the GPT API to their chatbot widget (such as Zoho SalesIQ), you can also simply use the Dialogflow CX Messenger to get started.

The new Dialogflow CX Messenger is different from the legacy Dialogflow Messenger and includes support for things like conversation history across the entire session, and support for Markdown format inside the text response blocks.

In other words, the out-of-the-box website chat widget already provides some decent functionality but things like CRM integration, automated email notifications, email transcript summary etc are not available in the built-in Dialogflow Messenger integration. If you need such advanced features, you can consider building a custom integration between Zoho SalesIQ and Dialogflow CX.

UI for entity definition and entity extraction

You probably need to use entities when you are building a multi-turn conversational bot, and the way you do this in the GPT API is very primitive at the moment.

Dialogflow CX provides robust support for entity definition and entity extraction.

Powerful slot filling

One of the most powerful features of chatbots is slot filling but it is also very hard to use.

While the slot filling implementation in Dialogflow ES was quite poor, the implementation improved tremendously in Dialogflow CX.

If you are building a simple slot filling bot, then Dialogflow CX is a much better option than the GPT API.

In fact, I very much doubt you can even build a regular slot filling chatbot using the current version of the GPT API.

Built-in support for conversation state management

What should you do when the user says something unexpected?

More importantly, how can you ensure your bot recovers back to the conversation correctly while also remembering the old context?

Since Dialogflow CX uses a state-machine-first approach, it has support for conversation state management out-of-the-box. In fact, I would argue it has the best conversation state management features among all bot frameworks, because the state machine itself is exposed as a primitive in the API.

GPT’s support for conversation state management is very rudimentary and also is not an actual API that you can configure or even control.

Answer FAQs in the middle of a conversation flow

One of the most challenging features to implement is the ability to answer frequently asked questions in the middle of a conversation flow.

Dialogflow CX offers robust support for this by allowing you to define FAQs which can be used anywhere during the conversation.

It is very hard to build an equivalent feature using the GPT API (and especially hard if the person designing the conversation flow is not a programmer).

Generative fallback if bot cannot handle the user question

While all the previous points would be valid even without Generative AI, the biggest improvement is the ability to use Generative Fallback if your CX bot cannot handle the user’s question.

This is a pretty large topic, but I personally think this can be quite a big deal for people who are iteratively building complex conversation flows (in other words, they don’t have the full flow designed before launching the chatbot).

When to use GPT API?

Does this mean there is no need to use the GPT API at all?

That would not be the right call either.

In fact, one of the biggest improvements provided by GPT is what I call extractive question answering. As of now, there isn’t a well-defined way to incorporate that into Dialogflow CX.

Suggested reading:

When to use Dialogflow ES

When to use Dialogflow CX

When to use GPT

Ask the Demo Bot

At the bottom of this page, there is a demo Generative CX bot which can help you figure out which of these tools is the best choice for your use case (note: the bot is very basic, and is still “Under Construction”)