The process of developing a ChatGPT-like chatbot must include comprehensive takes on structuring datasets, weaving AI/ML algorithms, and interface design.
What was once confined to scholastic discourses and test laboratories have turned into products more accessible to the general masses. This wave of Artificial Intelligence has seeped into human proximity. At the crest of the wave are Artificial Intelligence (AI) chatbots with the ability to execute informative assistance and parallel conversations. Some chatbots have already garnered immense publicity and so far extended their utility that transcends service and support. This has further led industries to build an AI chatbot like ChatGPT.
On top of the famed AI chatbots with reportedly the largest market share, and now with a disruptive status, is ChatGPT. It is an AI-powered language model developed by OpenAI. It is capable of comprehending and generating relevant responses to user prompts and questions. While noting the industrial utilities and possibilities of wider applications is critical, the topmost question amongst developers and entrepreneurs is how to build an AI chatbot like ChatGPT.
Provided the understanding that AI chatbots enrich customer experience and augment engagement, enterprise owners as well are actively looking to integrate AI-powered tools into their product infrastructure. For that, a detailed step-by-step guide on how to build an AI chatbot like ChatGPT from a technical and business viewpoint is discussed below.
ChatGPT, short for Chat Generative Pre-trained Transformer, is essentially a chatbot. The chatbot is laid atop an AI-powered framework trained with over 45 terabytes of text data to generate the most probable response to a set of words fed as a prompt or question. This further enabled ChatGPT to recognize patterns and combinations from the text data to render relevant responses.
On the other hand, ChatGPT defines itself as a language model developed by the Artificial Intelligence company OpenAI. It continues elaborating on its functionalities for natural language processing (NLP) tasks such as translation, question-answering, and chatbot development.
On the technical aspect, ChatGPT comes from the GPT family of language models and is an improved version of GPT-3 (termed as ‘GPT 3.5’). ChatGPT is the byproduct of fine-tuning GPT 3 with supervised learning—with the involvement of human trainers roleplaying both as the user and the AI assistant, as well as reinforcement learning, and reinforcement learning—where trainers would rank and mark the responses by the language model for dialogue. This process altogether is referred to as Reinforcement Learning from Human Feedback (RLHF).
OpenAI, moreover, is constantly fine-tuning ChatGPT from inputs collected from users. As of writing this article, ChatGPT’s data refinery is updated till September 2021. Ergo, questions on events post-2021 are likely to be furnished with incorrect responses. On a comparative note, the accuracy rate of ChatGPT alternatives like Bard AI and Bing AI is further behind.
ChatGPT’s core engine is powered by the machine-learning natural language processing (NLP) system GPT. So when a query input is entered, it analyses the enormous text data, identifies the most relevant pattern, and relays the response based on it.
To briefly list down this process in a step-by-step form:
To build an AI chatbot like ChatGPT, one of the most significant aspects to take into consideration is its features. From the definition of ChatGPT, we understand that the chatbot is capable of comprehending conversations and presenting relevant responses. The key components on the tech side include continuously refined datasets, a conversational interface, ML algorithms, and an extensive feedback system.
To summarize ChatGPT features, these include:
The cost of developing an AI chatbot, like any other digital product, is accounted for by factors, operational and computational. To elaborate, the computational aspect of developing an AI-powered chatbot would include datasets, app complexity, customizations for the end user, the range of features and functionalities, and so on. While the operational cost is to be determined by the rate of outsourcing, hiring freelancers, or building an in-house team of chatbot developers.
On the computational front, the process starts by collecting data, both proprietary and those available in the public domain. But data collection is a rather tedious and pricey process. Tedious in terms that, as an enterprise, you must sequence and annotate the vast availability of unstructured data to protract your line of business. And the fees for outsourcing data annotation projects can vary from tens to hundreds to thousands of dollars. Not to mention the already high cost of accessing proprietary data sources.
In addition to data gathering, data storage is another crucial computational requisite. Most chatbot development houses take the utility of cloud-based resources like AWS, Azure, or Google Cloud for storage. In ChatGPT app development, the interface is hosted through Microsoft Azure’s cloud infrastructure. The bill of availing these cloud computing solutions can rise up on a sharp scale. To develop ChatGPT in particular, the timeline can be traced back to 2018 when GPT was first introduced. So it’s fair to state that to build an AI chatbot like ChatGPT, it might take several months.
Considering the above-discussed factors, the budget range for developing an AI chatbot like ChatGPT would fall somewhere between $90,000 to $450,000. In addition, the overall AI chatbot development timeline would be prolonged and could take up to several months.
While overwhelming, the rate of AI chatbot development is indeed exorbitant. Yet it isn’t an intractable issue. With suitable checks and balances, the gross expense of AI-based chatbot development services can be considerably reduced. In the following section, pointers elaborating on how to lower the cost of AI chatbot development are presented.
Before diving into the process of ChatGPT development, it is critical to define the strategy and lay out an exhaustive blueprint. This ensures that the chatbot is aligned with the business objectives and customer preferences. In the succeeding section, the procedure for building an effective AI chatbot strategy has been outlined.
The must-haves for an AI chatbot to be exemplary are – a conversational flow, integrated machine-learning algorithms, and room for continual improvement based on user feedback. To build an effective and engaging chatbot, the following is an insight into the step-wise development process.
The first step in building a ChatGPT-like AI chatbot is to choose a Natural Language Processing (NLP) framework. There are several open-source NLP frameworks available, such as NLTK, Spacy, and Gensim. Each framework has its strengths and weaknesses, so it’s essential to choose the one that best suits your needs.
The next step is to prepare your data. Your chatbot needs a large amount of data to learn from, so you’ll need to gather and organize your data. This data can include text from chat logs, emails, social media posts, and other sources. Once you have your data, you’ll need to clean and preprocess it to remove any irrelevant information, such as special characters, stop words, and punctuation.
The third step is to train your chatbot. This involves feeding your data into your NLP framework and using machine learning algorithms to train your chatbot. The algorithms will learn how to understand natural language and generate responses based on the input they receive.
The fourth step is to fine-tune your chatbot. This involves tweaking your chatbot’s parameters and training it on specific topics or use cases. You may also need to add additional training data to help your chatbot learn more about a particular topic.
The final step is to integrate your chatbot into your website or application. This can be done using APIs or SDKs provided by your NLP framework. You’ll also need to develop a user interface to allow users to interact with your chatbot. This must further ensure a higher rate of customer satisfaction and better customer experience.
Knowing the applications of AI-powered chatbots across industry verticals is imperative when constituting the business strategy. While it can be ascertained that chatbots can be integrated into websites, mobile applications, or any pre-designed interface, utilities for the end user varies. On the other hand, OpenAI also provides API keys to integrate AI chatbots for a modest fee.
Immediate applications of AI chatbots can be assigned for proactive customer interaction and redressal management, query resolution, automation of repetitive tasks, and gathering customer feedback among others. Chatbots not only render a cost-effective but time-efficient alternative for performing routine tasks efficiently.
So far as the industry application is concerned, AI chatbots can be planted in the digital portals of products in the on-demand sector to enable a prompt grievance resolution facility. Chatbots powered with Artificial Intelligence in the finance sector can assist in queries relating to transactions, bill payments, investment portfolios, etc.
Healthcare sector can also have wider applicability for chatbots in simplifying booking appointments, furnishing diagnostics reports, and managing patient medical records. Similarly, in education, mobility, food and drinks, and other sectors, chatbots can supplement task automation and improve customer satisfaction.
According to reports, the global market value of chatbots in 2022 stood at USD 5,132.8 million and is projected to grow at a CAGR of 23.3% from 2023 to 2030. This statistic establishes that chatbots would continue redefining business communication and customer support.
Not to mention the advancements in the technical aspects to be attained through more personalized responses, multimodal communications with photos and video, deep incorporation of the entirety of a given website, initiation of more human conversations, and others.
At Appventurez, we have a dedicated team of AI/ML developers crafting digital experiences to construct future-ready businesses. We prioritize the strategic development of products and are focused on client satisfaction. With the expertise amassed over the years in Artificial Intelligence and Machine Learning solutions, we guarantee world-class product delivery that not only provides a seamless user experience but also caters to its business needs. So far AI chatbots are concerned, we guarantee the completion of your project on time and utmost quality.
Appventurez offers end-to-end AI chatbot development services, including business strategy development, interface design, natural language processing (NLP) integration, data annotation, and maintaining dataset algorithms. We also provide ongoing maintenance and support to ensure the chatbot is continuously updated and fine-tuned to better respond to user prompts and align with the digital portal. With a focus on delivering high-quality, user-friendly AI development solutions, we are committed to helping businesses improve their customer service, increase engagement, and drive growth.
Director and one of the Co-founders at Appventurez, Chandrapal Singh has 10+ years of experience in iOS app development. He captains client coordination and product delivery management. He also prepares preemptive requisites and guides the team for any possible issues on a given project.
Q. What is the average cost of AI chatbot development?
The average cost of AI chatbot development can vary widely depending on factors including interface design, chatbot complexity, data annotation, customization level, operational charges, etc. On average, the cost can range from $90,000 to $100,000, while more complex chatbots can cost $450,000 or more.
Q. What are the benefits of integrating an AI chatbot into a website?
The advantages of integrating AI chatbot into a website can vary from site navigation to customer support and availing of services. AI chatbots are also an efficient and cost-effective alternative to a standalone grievance management system. It further enhances the engagement rate and assists in upgrades for scalability.
Q. What programming languages are used for chatbot development?
Java is primarily utilized for chatbot creation because it enables portability and the high-level functionalities essential to create an AI chatbot. Also, due to its sophisticated interface, coding is smooth in Java. Therefore, for chatbot development, Java is the most suitable language.