Developing complex software systems takes a lot of time and to speed up the development process many companies today use AI services. But in some cases, AI alternatives are not worth using in complex systems. We detailed a review of ChatGPT, how it works, and some use cases with a chatbot. Then, we go deeper into the complexity of the software development process and its main drawbacks.
What Is ChatGPT? How Does It Work?
ChatGPT stands for Chat Generative Pre-Trained Transformer, which is developed by Open AI. ChatGPT uses Natural Language Processing (NLP) that helps us to analyze big amounts of information and understand and generate human-like language.
What Are the Common Use Cases for ChatGPT in It?
As you know, many companies implement AI assistants in their work, and we want to share the most common cases:
Integrating virtual assistants into your system helps with scheduling your emails, email management, and customer service. It can be a good alternative to change your routine work by using ChatGPT. It does not take much time, but it is important to pay attention to the detail of what the robot writes as sometimes it makes mistakes.
ChatGPT can assist in streamlining routine tasks related to emailing. Using an Excel table allows emails to be generated efficiently, saving time and effort. For lead generation teams conducting research, sending out numerous emails to potential candidates can be made simpler with the aid of ChatGPT. With the ability to generate different passages, proposals, names, and languages, ChatGPT is a valuable tool for those who need to communicate with candidates or individuals from various cultural backgrounds.
While there are many options available, technical descriptions and a few sentences is everything necessary for ChatGPT to provide credible responses. However, it’s always recommended to read through the generated text to ensure its accuracy, as ChatGPT’s capabilities are vast and may produce highly specific results.
ChatGPT knows a lot and can help in the search to find even things that you didn’t imagine. Another benefit is that it can help you with any of your questions, but it is limited to 2021.
Another problem is that the bot doesn’t want to recognize its mistakes, fails, or does not give you true information. It can generate some text, but the answer can be incorrect.
A chatbot will never answer the question “I don’t know”, if the information is not available to it or maybe the bot misunderstood the question, it will generate information it may not be true. This problem is serious, so the knowledge base should be retested or researched.
IT Service Desk
The IT Service Desk offers automated assistance to users experiencing IT-related issues, such as password resets or account lockouts. As a part of the IT service, this team assists clients with frequently asked questions and resolves their concerns.
When communicating with the service desk, users may interact with a chatbot, which is programmed to respond to predetermined scripts, company wikis, or similar sources. With ChatGPT, however, this process is simplified and streamlined. By providing relevant articles or input from your company’s scripts, for instance, ChatGPT can generate responses automatically, resulting in a more efficient and effective interaction. This scenario is typical and straightforward, making it an ideal solution.
AI assistants can provide automated assistance to employees who need help with HR-related issues, such as benefits and time-off requests. Also, a lot of HR routines can be replaced by ChatGPT in case you need to chat with a lot of people or handle a lot of processes. The new premium version of Chat GPT4 has more functions such as recognizing and analyzing audio from calls or using text messages from any source. It can be also useful for the HR department.
So, those are the five common use cases for ChatGPT in information technology. Now, let’s move on to the second theme, software systems’ complexity.
Complexity in Software Systems
The topic of complexity in software systems requires careful consideration. Specifically, we need to examine how ChatGPT can be helpful (or not) in dealing with complexity in software systems.
A software Systems
Software systems are often very complicated, with a lot of processes that need to be handled by different programs or sub-processes. This compilation of programs and sub-programs constitutes a software system. Using software systems, certain aspects of our daily activities can be automated and optimized. For instance, inserting data into a system can result in the system autonomously optimizing requests and delivering results. Nevertheless, decision-making is a more complex process that cannot be entirely automated.
When it comes to making decisions, people use sources, experiences, and screening information to make well-informed decisions. ChatGPT can assist in decision-making by providing answers to queries based on input, but it cannot replace human discernment.
When making decisions, emotional intelligence is also a factor, and ChatGPT has made significant progress in this area. ChatGPT version four, for instance, has demonstrated a high degree of emotional intelligence and can comprehend things like people’s sentiments, whether they are deceptive, and even some social context. It is essential to observe, however, that ChatGPT does not replace human judgment, as decision-making involves much more than text analysis.
One developer spent a week recently researching how well ChatGPT can analyze interviewees’ comments. Using specific criteria for analysis, ChatGPT version four produced results comparable to those of the developer, which was encouraging. It is crucial to note, however, that ChatGPT is only effective when given specific inputs and criteria to analyze. Without these limitations, ChatGPT is ineffective in making decisions.
Social context is a complicated topic that is sometimes difficult to comprehend. People may exhibit a range of emotions, actions, and communication styles, all of which should be taken into account when assessing social context ChatGPT, however, may assist with this by comprehending the data and meanings of the social environment. ChatGPT can provide light on certain populations, but it is unable to detect deception. Therefore, rather than depending exclusively on ChatGPT, it is better to independently examine the social environment.
It’s crucial to think about ethics while making judgments, but it’s tempting to skip that step in favor of utilizing ready-made solutions or artificial intelligence tools like ChatGPT. ChatGPT has benefits and limitations, and it’s important to consider ethical issues while using it.
Common Drawbacks of Using ChatGPT in Creating Software Solutions
ChatGPT can give some insight and results, but it also has certain drawbacks, so it’s important to be careful when using it.
- 1. High Computational Cost: GPT-based models like ChatGPT are computationally expensive, meaning they require powerful hardware and infrastructure to run efficiently. The reason for this is that these models are very complex and contain a large number of parameters. The more parameters a model has, the more computationally expensive it is to train and run. Fine-tuning a model may also require a large number of computational resources, depending on the size of the dataset being used for training.
- 2. Limited ability to handle structured data: ChatGPT is a language model, meaning it is designed to handle natural language input and generate natural language output. It is not designed to handle structured data, which refers to data that is organized in a specific way, such as in a database. If your service requires handling structured data, then it’s better to use models that are designed for that purpose.
- 3. Security and Legal Approval: As ChatGPT is an open-source model, there may be concerns about security and legal approvals/agreements for customer data usage. Open-source models can be more vulnerable to attacks or misuse of data, and it’s important to have appropriate security measures in place. Legal approval may also be necessary to ensure compliance with regulations and protect the privacy of users.
- 4. Training and Learning: ChatGPT uses internet data before 2021, and while it is still a very powerful model, it may not be up-to-date with current information. Additionally, for specific customized enterprise training, it will need a huge amount of data for Reinforced Learning and Supervised Training, which will need continuous development effort. Reinforcement learning is a type of machine learning where an agent learns to behave in an environment by performing actions and receiving rewards or penalties, while supervised learning is a type of machine learning where a model is trained on labeled data, intending to make accurate predictions on new, unseen data.
Overall, ChatGPT is a highly strong and popular language model, but it has limits when it comes to processing structured data and may need a lot of computing power and training data. It’s crucial to take into account these aspects while determining if ChatGPT is the best option for your specific use case.
ChatGPT is a Chat Generative Pre-Trained Transformer that analyzes a lot of data and produces text that sounds human. Virtual assistants, email responders, knowledge bases, IT service desk support, and HR help are just a few of the use cases for ChatGPT in the IT industry. ChatGPT can help to speed up the software development process but in some cases, it can’t replace human judgment, emotional intelligence, and decision-making.
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