Rudri Jani

Hello, everyone. I'm a graduate of the MBA program from Ahmedabad University. I've been working in this field for over two and a half years. I've worked on various technologies for NLP and machine learning, including Hugging Face transformers, tentative AI, LLMs, fine-tuning the models, fine-tuning pre-trained models, and working with different prompts and prompt engineering, LLMs, generative AI, OpenAI, etc. I've been a good performer in my company and have always been trained by my coworkers, peers, and seniors. I'm very hardworking and sincere in my work. I like working with different technologies, trying new things, and am enthusiastic to learn about new technologies and implement them.

Prevent overfitting in the chatbot to prevent overfitting. With the neural network, we can increase the number of layers in the neural network, increase the data. We can also edit the dropout rate. So JetBlue gives proper replies and relevant replies. We can also use regularization technology and one or two regularization techniques to reduce overfitting.

So we can use different patterns for real-time chatbot message handling. Like, we can store checks in NoSQL databases that store unstructured data. Other than that, we can also train the model with natural language processing or intent identification to identify what exactly the client or user wants. We can also use the JetBot to extract the API and extract information from the API. We can also use real-time API calls to get the current and latest data from the API. Like, if we're developing the chatbot for weather forecasting, then if the user says, "I want to know the weather forecast for today in a particular region," we will directly give that query to the API using the JetBot interface, and then the current real-time data will be given to the user. That's one of the design patterns we can use to create the chatbot. We can also use voice-based and text-to-speech-to-text patterns to be used in the chatbot so that we can use multimodal functionality with the user. So if the user is unable to write or search within the speed limit, they can still do it.

To ensure the metrics, like, different KPI indicators that we can use to measure the performance of the JetBot link, how much the users are visiting the port, and how much it is being used. The fall-back rate, frequency of question asking, then how much they are, we can also collect the feedback that can help users that can help to do the performance monitoring as well. The type of customers visiting the chatbot, and how they are feeling through sentiment analysis, which type of impression we are giving to the user. Then, the bounce rate, what type of questions they are asking, like frequently asked questions. User rating, conversion rate, conversion duration, how long they stay on the chatbot, number of sessions per channel, etcetera, we can use to help monitor the performance.

For this type of thing, we have to train the chatbot to identify whether these words are slang or not. For example, if we are using the latest advanced JPT models that can identify which words are slang and which are not. Other than that, we can also train the machine learning algorithm for deep learning neural networks so that they can identify the type of words they are getting. And based on that, they can filter out those words. We can use filters for that. We can use one database where nonstandard language is fed, and based on elastic search queries or text analysis, we can find which language is not allowed or not good, and they can filter out those questions or reports of slides as well.

SQL aggregation functions can be used to get the chatbot response from multiple tables based on the user based on the user query. For example, if a user is seeking product details and it is distributed across multiple tables, then from analyzing with entity extraction from the user query, we can identify which queries, which entities are present in the query. Like, if a user queries, "I want to search the product details about the soaps," for example, then we can have the soaps and the details and descriptions that are distributed, like product details and product datings, which have different tables, pricing, which has different tables, or all the prices in one table. We can aggregate it with SQL aggregation functions and get the response from that. And then we can also use the technique of NLP and give the proper answer in natural language to the user so that it cannot feel like it is machine generated and better in natural language.

It is set for the time, but it is waiting for the operation to start so that it has the mistake because as when the user input will not be received until then, it will wait for the input. And there is no async/await created in this. So it is not doing some asynchronous code handling.

Here if there is a database that has a long large amount of data, then this loop might have more computations. And it takes too many iterations to identify and fetch the data. If we are doing this for loop in batches, then the performance can be improved.

When scaling an AI checkbook from handling multiple millions of users, we have to ensure that it should handle all the users at a time. It should have a sync await functionality so that millions of users can be handled asynchronously, and everyone does not have to wait for a long time, making it scalable. Other than that, we can also use the Docker implementation and make the containers very lightweight so that they can be handled effectively. And we can have a suitable deployment platform. We have to ensure that the server where the chatbot is deployed is running fine and able to handle multiple users as well. Now we have to use the effective Jetbot algorithms that can handle multiple requests at a time with fast processing and getting the response effectively. We can also have data volumes where the data has been stored that are fast enough and quick enough to give the response as well. And it should also be able to fetch the data efficiently. You can also use real-time APIs to fetch records, and we have to ensure that those APIs are not taking too much time and give a quick reply as well.

If we are having graph databases, then we can effectively find out the responses in a quick manner. For example, if we are using a notes-based structure and there is a need to find out the details of candidates who are applying, then it will increase the search time. And, for example, there is a node with skills, another node with number of experience, and another node with education. If different candidates have the same skills or same number of experience, then we can make a tweak. And from that, we can access the information quickly. Other than that, at the end of it can give explicit and complete control over the answers provided by the chatbot and allow it to avoid hallucination. Then, if you are using a knowledge graph, all the repetitive work for the knowledge graph can help to clarify concepts, structures, and entities. From there, we can easily identify the answers and respond to the user.

The data visualization tools, like, check, Tableau or Power BI can be integrated using APIs or other methods. We can also have graphs or pie charts to generate a dynamic response for tabular data or numerical data analysis. JetBlue can find responses using this, and it can be easily interpreted to understand the results for the user. You can also use heat maps so that the user will get to know which are the important and which are not required.