OVERVIEW
With the emergence of LLMs (Large Language Models), the field of generative AI is rapidly evolving. Compared to previous technologies, LLMs are capable of understanding long texts and processing them in a way that more closely resembles human thinking. As a result, they can be applied to various tasks and services—such as reducing labor costs through operational efficiency or enabling 24/7 customer support. For companies unsure about how to incorporate generative AI into their operations or services, we offer comprehensive support—from consultation to development and operation.ㅤ
Generative AI (LLM)
LLMs (Large Language Models) have a vast number of parameters, allowing them to learn complex language patterns and perform high-accuracy processing. As a result, they can handle tasks such as translation, summarization, question answering, and text generation.
While machine learning (ML) has existed for some time, the key difference lies in LLMs’ ability to process large amounts of text data in parallel. This advantage enables them to process entire passages at once and understand relationships between words across sentences, allowing for more accurate comprehension of the user's intent and better task execution.
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Label | Traditional ML | LLM |
|---|---|---|
Text comprehension | Since processing is done word by word, | Because words can be processed in parallel, |
Range of applications | One model handles one task. | Translation, summarization, question answering, text generation, |
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Improving Generative AI Accuracy
Generative AI needs to improve its accuracy through a learning process. Just like humans, without proper training, generative AI can produce incorrect answers. This process involves preparing accurate and well-balanced data, conducting appropriate training, and then fine-tuning the model for specific use cases. By doing so, generative AI can perform more accurate tasks.
There are two major challenges that arise during the training process of generative AI: bias and hallucination.
Bias occurs when the training data is skewed, leading to biased output. For example, if the data includes more information related to men, the output may disproportionately reflect male-oriented results. Training on social media data may lead to outputs that are overly conversational or informal. In this way, biased data leads to biased AI behavior.
Hallucination refers to when a large language model generates information that does not actually exist.
It is essential to address these issues carefully in order to improve the accuracy of generative AI.
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Web System Utilizing Generative AI
In the future, it is expected that many web systems incorporating generative AI will be offered in the market. These systems will process tasks using generative AI on the backend while customers or employees operate the web interface. For example, a prototype project our company is currently working on connects with warehouse inventory data. When a staff member asks questions about popular products via a chat format on the web interface, the generative AI works with the database on the backend to provide answers. In the future, the system aims to support demand forecasting as well, allowing staff to manage inventory by chatting with generative AI through the web interface.
Our Strengths
We have been providing services in the fields of data and web systems for many years. Our generative AI engineers originally come from backgrounds as web engineers and data engineers, allowing us to offer optimal solutions for building generative AI systems. If you have any issues or inquiries related to generative AI, please feel free to contact us.