Modern AI Models for Text Analysis: A Comparison of Chatgpt and Rag

N.A.

Makale detayları

Modern AI Models for Text Analysis: A Comparison of Chatgpt and Rag

Yazarlar:

N.A.

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 1, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341576

pISSN:

3062-2697

Anahtar kelimeler:

Özet:

 This study presents a comparative analysis of two text-processing models: ChatGPT and Retrieval Augmented Generation RAG. ChatGPT, built on the Generative Pre-trained Transformer GPT architecture, excels at generating coherent and contextually appropriate texts, making it widely applicable in fields such as education, healthcare, and business. However, it has a significant limitationit relies solely on pre-trained data, lacking the ability to access real-time information, which can affect the relevance of its responses in dynamic contexts. In contrast, RAG integrates text generation with external data retrieval, offering a substantial advantage in terms of real-time data relevance. This feature enhances both the accuracy and completeness of the generated responses, especially for tasks that require up-to-date information. The study evaluates both models based on several key performance indicators, including accuracy, completeness, processing time, and scalability. The conclusion highlights the strengths and weaknesses of each model and suggests potential improve ments for their future application across various domains. By offering a deeper understanding of the capabilities and limitations of these technologies, this research contributes to their optimal use and further development.

 This study presents a comparative analysis of two text-processing models: ChatGPT and Retrieval Augmented Generation RAG. ChatGPT, built on the Generative Pre-trained Transformer GPT architecture, excels at generating coherent and contextually appropriate texts, making it widely applicable in fields such as education, healthcare, and business. However, it has a significant limitationit relies solely on pre-trained data, lacking the ability to access real-time information, which can affect the relevance of its responses in dynamic contexts. In contrast, RAG integrates text generation with external data retrieval, offering a substantial advantage in terms of real-time data relevance. This feature enhances both the accuracy and completeness of the generated responses, especially for tasks that require up-to-date information. The study evaluates both models based on several key performance indicators, including accuracy, completeness, processing time, and scalability. The conclusion highlights the strengths and weaknesses of each model and suggests potential improve ments for their future application across various domains. By offering a deeper understanding of the capabilities and limitations of these technologies, this research contributes to their optimal use and further development.

 This study presents a comparative analysis of two text-processing models: ChatGPT and Retrieval Augmented Generation RAG. ChatGPT, built on the Generative Pre-trained Transformer GPT architecture, excels at generating coherent and contextually appropriate texts, making it widely applicable in fields such as education, healthcare, and business. However, it has a significant limitationit relies solely on pre-trained data, lacking the ability to access real-time information, which can affect the relevance of its responses in dynamic contexts. In contrast, RAG integrates text generation with external data retrieval, offering a substantial advantage in terms of real-time data relevance. This feature enhances both the accuracy and completeness of the generated responses, especially for tasks that require up-to-date information. The study evaluates both models based on several key performance indicators, including accuracy, completeness, processing time, and scalability. The conclusion highlights the strengths and weaknesses of each model and suggests potential improve ments for their future application across various domains. By offering a deeper understanding of the capabilities and limitations of these technologies, this research contributes to their optimal use and further development.

 This study presents a comparative analysis of two text-processing models: ChatGPT and Retrieval Augmented Generation RAG. ChatGPT, built on the Generative Pre-trained Transformer GPT architecture, excels at generating coherent and contextually appropriate texts, making it widely applicable in fields such as education, healthcare, and business. However, it has a significant limitationit relies solely on pre-trained data, lacking the ability to access real-time information, which can affect the relevance of its responses in dynamic contexts. In contrast, RAG integrates text generation with external data retrieval, offering a substantial advantage in terms of real-time data relevance. This feature enhances both the accuracy and completeness of the generated responses, especially for tasks that require up-to-date information. The study evaluates both models based on several key performance indicators, including accuracy, completeness, processing time, and scalability. The conclusion highlights the strengths and weaknesses of each model and suggests potential improve ments for their future application across various domains. By offering a deeper understanding of the capabilities and limitations of these technologies, this research contributes to their optimal use and further development.

Makale detayları

Modern AI Models for Text Analysis: A Comparison of Chatgpt and Rag

Yazarlar:

N.A.

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 1, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341576

pISSN:

3062-2697

Artificial intelligence (AI)

machine learning

natural language processing (NLP)

ChatGPT

RAG

Sayı

Makale detayları

Modern AI Models for Text Analysis: A Comparison of Chatgpt and Rag

Yazarlar: N.A.

Yayıncı: Istanbul University

Ana Başlıklar: Artificial Intelligence (Other)

Dergi: Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 1, pp.1-13

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001341576

pISSN: 3062-2697

Artificial intelligence (AI)

machine learning

natural language processing (NLP)

ChatGPT

RAG

Sayı

Yazarlar:

N.A.

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 1, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341576

pISSN:

3062-2697