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How practical is GPT-3 for Conversational AI chatbots?

3 min

In this article we will explore how practical GPT-3 is for Conversational AI chatbots and voicebots, and what you need to know about it.

First, what is GPT-3: An autoregressive language model that uses deep learning to produce human-like text. In short, it’s an AI model that is trained on a lot of existing public data (the whole internet basically) to teach it to give responses as if it was a human.

Existing language models need training before they understand the context of what you are asking them to interpret. GPT-3 does not. That’s a big time saver. Additionally, the human-like factor is quite remarkable.

Did GPT-3 suddenly appear? How about GPT-2?

GPT-3 is the 3rd and latest version of GPT-N, which stands for Graphical Temporal Patches, (N stands for the version), which is a subset of the full GPT that includes only those patches with at least one temporal dimension.

So what’s a temporal dimension? Basically, it’s a dimension that measures change over time. Understanding the effects of time is an important factor when designing Conversational AI.

How practical is it for chatbots and voicebots?

The answer to this question depends on the specific use case you’re designing your chatbot around. More specifically, if you need to plan out temporal patterns or anticipate future events.

GPT-N can be used to model all kinds of systems, including music and conversation. It has been used successfully when designing a music synthesizer with specific temporal patterns (e.g. drums) played at a set tempo. Fun, but how useful is it for business?

The pitfalls:

While GPT-3 solves problems with accuracy, scalability, and efficiency, (issues associated with deep learning models), the benefits were initially not so clear for conversational AI because the depth of understanding you need in order to make predictions had yet to be posed on these types of systems.

Additionally, companies need a way to guide a conversation in a certain direction to answer questions, do transactions, or get a resolution to another issue raised by a human to a machine. Therefore, they need a path for the conversation, created by designing conversation flows.

The upsides:

In the meantime, when it comes to chatbots and conversational AI we believe GPT-3 can primarily be used for two things:

A) Non-intent chit-chat:

When a human asks a bot a question and is not understood, the bot needs to be programmed to do something to get to a better understanding. In the majority of cases it currently asks the human to rephrase the input it gave, leading to irritation, especially when this happens several times in a row.

GPT-3 can equip the bot with a near endless supply of possible variations of human interactions, the way a human would. After all, GPT-3 was trained by absorbing the internet’s human answers in the first place. By tweaking the settings of humor and tone of voice, the brand persona can be properly represented by the bot’s replies.

B) Grammar Correction:

The use of slang, slur and jokes poses a threat to the ability of NLP Engines like Intent Recognition to interpret the meaning of a sentence uttered by a human to a machine.

Look at these two example conversations:

Chatbot using normal NLP                         GPT-3 corrected chatbot

How can I help?



Yo brother from another mother, me wants to speak human man!

I don’t understand, could you repeat the question please?


How can I help?



Yo brother from another mother, me wants to speak human man!
GPT-3 grammar correction —->
I’d like to speak to a human please!

One moment please, I’ll transfer you to the next available agent.


(Or more humorous versions thereof)..


               GPT-3 acting as a filter, helping NLP interpretation.


The good news:

We managed to integrate GPT-3 in the Conversational AI platform!

This means you can design your flows in the platform using drag and drop as usual, utilize any of the available AI suppliers for intent recognition, entity extraction, text to speech and so forth, and use GPT-3 to augment this.

The future:

GPT-3’s development into an even more usable GPT-4 is ongoing.

It’s likely a matter of time before NLP encompasses this ability of GPT-3, but for now it’s a great way to broaden the horizon of a chatbot. And before you know it GPT-4 will appear, or it’s competitor variations. It is likely it will be even better at tweaking and influencing the desired tone of voice.

Article Conclusions:

– GPT-3 is a practical way of encoding data in natural language to encode meaning. It can be used for chatbots and conversational AI as well but has limitations.

– Use cases exist where it’s proven successful, such as in chit-chat and grammar correction.

–  The ecosystem has integrated GPT-3.


We are curious what you will build, let us know!!



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