ChatGPT vs. Bard: Which one is Better?

ChatGPT vs. Bard: Which one is Better?

With the evolution of AI, now and then, the industries like OpenAI and Google are exploring more technology experiences empowering companies with more advancement. ChatGPT represents a significant advancement in natural language processing and can potentially revolutionize how we interact with machines.

In contrast, the development of BARD is part of Google’s ongoing efforts to advance the field of natural language processing and AI and explore these technologies’ potential in creative contexts.

Google -BARD

Google BARD” is an artificial intelligence (AI) system developed by Google that can write poetry. BARD is “The Biomechanical Automated Rhythmic Digit,” a computational system designed to generate creative and original poetry based on machine learning algorithms and deep neural networks trained on a vast corpus of poetry and literature.

Google BARD is designed to create poems in various styles and genres, including sonnets, free verse, and haiku. The system can also generate poems in different languages, including English, Spanish, French, and German.

While BARD is still in the research stage, it is an exciting development in AI and poetry and could be used to help humans create new and inspiring works of literature.

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Key Features of Google Bard

Poetry Generation

BARD is an AI system designed specifically to generate poetry. It can create poems in various styles and genres, using literary devices such as alliteration, metaphor, and rhyme.


The system can generate poetry in different languages, including English, Spanish, French, and German.

Training Data

BARD is trained on a large corpus of poetry and literature, allowing it to generate original and creative works.

Neural Networks

BARD uses deep neural networks to learn from the training data and generate new poems. These networks are based on a system of interconnected nodes that can learn patterns and relationships in the data.

Creative Control

While BARD generates poems automatically, it also allows for some degree of creative control by the user. For example, users can input certain words or themes on which they would like the poem to be based. 

Ongoing Research

BARD is still in the research stage and is continually being improved and developed by Google’s AI researchers and engineers.

Since it is an AI system developed by Google, it is likely accessible through Google’s cloud-based AI services or APIs, such as Google Cloud AI Platform or Google Colaboratory. These platforms can be accessed using a web browser and do not require any specific software installation. 

Limitations of Bard

As with any artificial intelligence system, Google BARD has certain limitations. Here are some potential limitations of BARD: 

Lack of Creativity

While BARD is designed to generate original and creative poetry, some critics argue that AI-generated poetry needs more human touch and the emotional depth of works created by human poets.

Lack of Cultural Context

BARD is trained on a large corpus of poetry and literature but may need to fully understand different literary traditions’ cultural contexts and nuances.

As a result, its output may need more cultural sensitivity and richness of human-created works. 

Language Limitations

While BARD is multilingual, it may only be able to generate poetry in some languages. Additionally, its output may only sometimes be grammatically or semantically correct, especially when generating poetry in different languages it is less familiar with. 

Overreliance on Training Data

BARD relies heavily on the quality and quantity of its training data. If the training data is biased or incomplete, it could result in biased or incomplete output.

Limited Application

While BARD is an interesting development in AI and poetry, it has limited practical applications beyond generating poetry for creative or educational purposes.


ChatGPT refers to an AI language model developed by OpenAI based on the GPT-3 architecture. It is designed to respond to natural language inputs conversationally, making it well-suited for chatbots and other conversational AI applications.

ChatGPT can perform various tasks, including language translation, question-answering, and summarization. It has been trained on a massive amount of data from the internet, which allows it to generate human-like responses and understand the context.

One of the unique features of ChatGPT is its ability to adapt to different tasks and contexts. It can be fine-tuned on specific datasets to improve its performance on particular tasks, making it a highly versatile language model. 

Key Features of ChatGPT

ChatGPT has several features that make it a powerful language model: 

Natural Language Processing

ChatGPT is designed to understand and process natural language inputs, making it highly effective at conversing with humans. 

Contextual Understanding

The model has been trained on a massive dataset that enables it to understand the context of a conversation and generate human-like responses.


ChatGPT can understand and generate responses in multiple languages, making it highly versatile. 


The model can be fine-tuned on specific datasets to improve its performance on particular tasks.


ChatGPT is highly accurate and can perform many highly precise tasks. 


The model is highly scalable and can handle large data and requests. 

Continual Learning

ChatGPT is designed to continually learn from new data and interactions, which helps to improve its accuracy and performance over time.

These features make ChatGPT a powerful and versatile language model with a wide range of applications in natural language processing and conversational AI.

Limitations of ChatGPT

While ChatGPT is a highly advanced language model, it does have some drawbacks:


ChatGPT may reflect biases in the data it was trained on, like any AI system. This can lead to biased or discriminatory responses in certain contexts.

Lack of Common Sense

While ChatGPT is highly effective at processing and generating natural language, it needs humans’ common sense knowledge and reasoning abilities.

Limited Domain Knowledge

The model’s training data primarily consists of web pages and other online sources, which may not provide the depth of knowledge needed for certain specialized domains. 

Dependence on Data Quality

The accuracy and effectiveness of ChatGPT are highly dependent on the quality and relevance of the data it is trained on.

Difficulty with Ambiguity

ChatGPT may need to help understand ambiguous or vague inputs, which can lead to errors in its responses. 

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Inability to Initiate Conversations

The model is designed to respond to user inputs and questions but cannot initiate conversations independently.

While ChatGPT is a highly advanced and capable language model, it is important to be aware of its limitations and use it in appropriate contexts where it can be most effective.

ChatGPT vs. Google Bard

As an AI language model, ChatGPT can provide information on various topics and engage in conversations with users. On the other hand, Google’s language model, BARD, is a large-scale neural network designed to generate human-like text in response to a given prompt.

While both models are based on similar underlying technology, there are some differences between ChatGPT and Google BARD. One key difference is their training data – ChatGPT was trained on a large corpus of text from the internet, while BARD was trained on various text sources, including books, articles, and web pages.

Another difference is their intended use cases – ChatGPT is designed to be a conversational AI, while BARD is intended for text generation tasks such as summarization, translation, and question answering.

Overall, both ChatGPT and Google BARD are impressive language models that are capable of generating high-quality text. However, their strengths and weaknesses depend on their training data and intended use cases.

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Nisha Sharma
Nisha Sharma- Go beyond facts. Tech Journalist at OnDot Media, Nisha Sharma, helps businesses with her content expertise in technology to enable their business strategy and improve performance. With 3+ years of experience and expertise in content writing, content management, intranets, marketing technologies, and customer experience, Nisha has put her hands on content strategy and social media marketing. She has also worked for the News industry. She has worked for an Art-tech company and has explored the B2B industry as well. Her writings are on business management, business transformation initiatives, and enterprise technology. With her background crossing technology, emergent business trends, and internal and external communications, Nisha focuses on working with OnDot on its publication to bridge leadership, business process, and technology acquisition and adoption. Nisha has done post-graduation in journalism and possesses a sharp eye for journalistic precision as well as strong conversational skills. In order to give her readers the most current and insightful content possible, she incorporates her in-depth industry expertise into every article she writes.