Today, there are plenty of solutions for chatbot creation. So why are many businesses using GPT-based chatbots? In this article, we will figure out if it suits your business and how to make use of it.
The Greenice team has extensive practical experience in this area, having developed chatbots using GPT models and other AI tools. We are here to share our knowledge, guide you through the development process, and provide an overview of the pros and cons, as well as an estimate of the cost of creating a GPT-like chatbot.
What is ChatGPT-Like Bot?
ChatGPT is a buzzword these days, especially when it comes to chatbots. So, let's clarify.
ChatGPT is both a web application and a large language model developed by OpenAI. It's designed to generate human-like responses to text-based conversations. The API for ChatGPT is the GPT-3.5-turbo model, which you can use to create a similar chatbot.
GPT (Generative Pre-trained Transformer) is an advanced language processing technology created by OpenAI. This deep learning algorithm is trained on vast amounts of text data, enabling it to analyze and understand natural language patterns and structures. The most powerful GPT models available are GPT-3, GPT-3.5-turbo, and GPT-4.
But what exactly is a ChatGPT-like chatbot?
A ChatGPT-like chatbot is a chatbot built on one of the GPT models. This type of chatbot is capable of having conversations in natural language, such as English. It can answer questions, provide information, generate text, and even perform tasks based on the input it receives from users.
With APIs, these technologies can be integrated into a variety of applications and services, including customer service, personal assistants, and educational tools.
Pros and Cons of ChatGPT-Like Chatbots
ChatGPT is a powerful tool, but it is not almighty. When using chatbots powered by GPT technologies or their alternatives, consider their strong and weak points:
- Extremely powerful in natural language processing. GPT models specialize in processing human language, which makes this technology perfect for chatbots. They understand human language, even slang or jokes, which helps to provide users with necessary responses. In terms of natural language processing, other AI chatbots are barely as capable as GPT.
- Good at translating text (again, thanks to NLP capabilities). One of the most outstanding features is that GPT models are multilingual and understand up to 26 languages. However, they work slightly better in English than in other languages.
- Good at analyzing and organizing information. GPT models are very practical in structuring data and can highlight necessary information from text.
- Large knowledge base. GPT models are trained on a vast amount of information, making them a valuable tool for research and information retrieval. GPT-powered chatbots are able to answer multiple questions with both company-specific data and additional information.
- Quick response time. GPT models can generate responses quickly, making them useful for applications such as customer service.
- Privacy and safety. This technology records conversations, including sensitive data like contact information, credentials, payment card details, and transaction history, and can send it to third parties. Although OpenAI claims to do this only to improve GPT performance, it has already raised data security concerns in some countries like Italy. Users must be careful and not share any confidential work or private information with ChatGPT if they do not want possible data breaches.
- Accuracy. These models can hallucinate or provide outdated information. However, the situation improves with each new iteration, and GPT-4's accuracy surpassed 80%.
- Dependency on prompts. The clearer and more detailed the request, the better the answer. Unclear or too basic prompts will provide low-quality answers.
- Limited memory capacity. GPT models might lose the thread of the conversation, especially older versions. However, GPT-4 is better in this regard, as it is able to remember up to 64,000 words, which is more than enough for a long conversation with a bot.
- Ethical concerns and bias. There is still a chance that a chatbot powered by GPT models will give inappropriate responses, such as sexist jokes. This happens due to reflecting biases present in the data the model was trained on. But OpenAI keeps working on that to provide more appropriate results.
- Limited understanding of context. ChatGPT may struggle to understand the context of a conversation, leading to irrelevant or inaccurate responses. For example, it may not provide information about political scandals when asked about the "-gate" suffix.
- Lack of emotional intelligence. ChatGPT lacks emotional intelligence, making it impossible for it to fully understand the emotional state of a user or respond appropriately to emotional cues.
- Dependence on training data. ChatGPT's performance (as well as its alternatives) is heavily dependent on the quality and quantity of the data it was trained on, which may limit its effectiveness in certain domains or applications.
To sum up, GPT models are highly suited for chatbot creation. Their ability to understand human language (using natural language processing) and to reproduce it makes them a fantastic option. However, issues with safety and accuracy make this technology less reliable for certain applications.
Best and Worst Areas of Application for ChatGPT-Like Chatbots
Chatbots are transforming diverse industries such as eCommerce, marketing and sales, travel and hospitality, real estate, education, and HR management. However, high-stakes areas such as healthcare or finance could be risky for this new technology. Let's find out where AI chatbots can work best and look at successful examples.
Best Areas of Application
Chatbots can assist customers in navigating web pages and providing personalized recommendations. However, it is recommended to set up a payment process outside the bot to avoid personal data breaches. Here are some examples:
- Shopify has integrated GPT-3.5-turbo with an AI shopping assistant in a Shop app. Now it makes personalized product recommendations for users.
- Instacart has integrated GPT-3.5-turbo to recommend new ideas and items to buy. Users can ask about the best breakfast, and the AI assistant will provide them with recipes and shoppable ingredients.
Marketing and Sales
Chatbots collect and analyze customer feedback, track and nurture leads, and personalize messages. Here are some examples:
- Viable uses GPT-3 to help businesses analyze customer feedback. The AI creates summaries with valuable insights about everything from live chat logs to text sentiment.
- Replier.ai uses GPT-3 to automate responses to all customer reviews, taking previous responses as an example.
- Greenice AI assistant - we use ChatGPT API in our bot to consult customers. You can try it on our website!
Travel and Hospitality
Chatbots help travelers find necessary information without browsing multiple sites. Here is an example:
- Expedia created a GPT-4-powered AI assistant that offers users personalized hotel recommendations.
Chatbots fulfill mundane tasks like sharing timetables and monitoring progress. Here are some examples:
- Quizlet integrated GPT-3.5-turbo with Q-Chat. It offers AI tutoring by communicating with students and questioning them based on relevant study materials.
- Duolingo integrated GPT-4 with its AI assistant that helps users learn language and correct mistakes.
- Khan Academy uses GPT-4-powered AI assistant Khanmigo for education. It serves as both an assistant for teachers and a tutor for students.
Chatbots relieve managers of answering repetitive questions and assist with pre-screening resumes and scheduling interviews. Here are some examples:
- Stripe uses GPT-4 in several ways. The technology allows for enhancing customer support by analyzing clients’ websites. It also answers questions about the Stripe documentation inside the company. Finally, GPT-4 scans community platforms for fraud. Social Media and
- Morgan Stanley has an AI chatbot with GPT-4. It helps personnel effectively use the company's database.
While GPT models are great at processing human speech, they can also entertain people. Here are some examples:
- Snapchat integrated GPT-3.5-turbo with ‘My AI’ chatbot. The bot offers recommendations and entertainment, e.g., it can write poems for users.
- Latitude implemented GPT-3 in an AI Dungeon game. The AI continues the story for users based on their prompts.
Another option is to make the bot a product itself, like ChatGPT. Such a bot can perform certain tasks to assist people, e.g., answering questions, building routes, or writing songs. Here are some examples:
- Be My Eyes integrated GPT-4 with its AI assistant for visually impaired people. The AI helps users with navigating locations or identifying and describing products.
Chatbots can be perfect assistants for organizing data and thus enhancing productivity. Here is an example:
- Notion AI uses GPT-3 as a writing assistant that can improve grammar, spelling, or even the style of the text.
These are just a few examples of the implementation of GPT-based chatbots. New use cases appear every day. At the moment you read the article, someone might be using a GPT bot for a 3D real estate tour.
Worst Areas of Applications
Chatbots can be helpful in healthcare for reducing paperwork for doctors and providing assistance to patients. However, it's not advisable to use chatbots like ChatGPT for sensitive data and health decisions. GPT models are not HIPAA compliant and can't be trusted to handle patient information (PHI). They also can't provide 100% accuracy, so it's too early to rely on them for important decisions or advice.
For this industry, Amazon Lex technology is a better choice as it is HIPAA compliant. For example, Greenice created a chatbot for hospitals that collects patients' symptoms and schedules a visit to a doctor. In case of a unique or unexpected question, the chatbot connects the user to a live agent. We created it with the help of Amazon Connect telephony technology that seamlessly uses Amazon Lex chatbot.
While chatbots can navigate websites, they cannot be fully relied upon for finance. Due to limited data and hallucinations, ChatGPT-like technology cannot provide reliable and secure advice, especially for the most recent trends.
Other high-stakes areas
Low accuracy, hallucinations, bias, and outdated information make the GPT model unsuitable for life and death decisions. Do not take these bots as experts but rather as assistants.
In summary, ChatGPT-like bots are great for many tasks, but they cannot be trusted with your money and your life yet.
Alternatives to ChatGPT
The market for AI technologies is growing rapidly, offering options that vary in size, language, and capabilities. Here are just a few examples:
- GPT-3: 2020-version of GPT model. It is able to process text mainly and can be fine-tuned for a variety of tasks.
- GPT-4: 2023-version of GPT model that can process text, speech and image. It is the largest GPT model at the moment, possessing over 1 trillion parameters. To get an API you need to join a waitlist.
Powerful and multitask language models:
- Bloom: autoregressive model by BigScience trained on vast amounts of text data in 46 languages and 13 programming languages. Can also be trained to perform new text tasks.
- BERT: an LLM by Google, pre-trained on large sources of text like Wikipedia, used for various NLP tasks such as question answering and sentiment analysis. It also can be fine-tuned with a question-answer dataset.
- LLaMA: foundational large language model by Meta that helps democratize access to large infrastructure for researchers. It can have many applications from data analysis to chatbots.
Models with ChatGPT-like capabilities for specific goals
- Alpaca: developed by Stanford, fine-tuned LLaMA model for following single-turn instructions. Meant for academic research only.
- ChatGLM-6B: language model with 6.2 billion parameters for Chinese QA and dialogue, deployable on consumer-grade graphics cards. Open Assistant: SFT model based on Pythia 12B, fine-tuned on human assistant conversations.
- BELLE-7B-2M: based on Bloomz-7b1-mt and fine-tuned on Chinese and English data, with good Chinese instruction understanding and response generation.
- Alpaca-LoRA: 7-billion-parameter LLaMA model fine-tuned to follow instructions, trained on the Stanford Alpaca dataset.
Each alternative may be the perfect choice for a specific need. However, GPT models - including the one powering ChatGPT - are multitask, multimodal, and multilingual, which sets them apart from the competition.
How to Train ChatGPT on Custom Data
To tailor a chatbot for your business, you need to train it. Chatbot training is the process of teaching a chatbot how to understand and respond to user input in natural language. The training process typically involves three main steps: data preparation, model training, and evaluation.
When it comes to GPT models, the training process depends on the model.
For example, with GPT-3, you need to fine-tune it before using it. After getting access to the model API from OpenAI, you'll need to use a library like TensorFlow or PyTorch to set the training parameters. From there, you'll train the model using 80% of your data. You can use 10% of your data to check how well the model is performing during training and another 10% to test the model's accuracy.
Another method is prompt engineering with your database. In this case, save all important data related to your company in a single database. When a user enters a prompt, the system looks for similar information in the database, adjusts the prompt accordingly, and sends it to GPT-3 (or GPT-4).
At Greenice, we built a chatbot with GPT-3 that uses a library with phrases (aka “hints”) related to our company. When a user asks a question, the bot separates hints, looks for keywords or phrases in the library, and responds accordingly.
As for the ChatGPT API (GPT-3.5-turbo), we tried this model after GPT-3 and found the turbo version much easier to work with. It doesn’t require fine-tuning and is trained based on roles (system, user, assistant).
To train a chatbot, input messages in a specific format that includes the role of the speaker (system, user, or assistant) and their message. Short or long conversations work, with alternating messages between the user and the chatbot. The system message sets the behavior of the chatbot, while user messages give instructions. Chatbot messages store prior responses. Including the conversation history helps provide context, but if it's too long, it must be shortened.
Features of an AI Bot Built with ChatGPT API
Chatbots may have different features depending on their purpose and industry. Let’s examine the most common features for all types of bots:
1. Client widget
A client widget is a visible chatbot that appears on the screen after a certain action, such as opening a page or clicking on an activation button. Good chatbot UX should include the following attributes:
- Feel and sound natural and human-like to give the impression of a real conversation.
- Provide quick answers.
- Have a name and avatar.
- Have emoticons (emojis).
- Not leave a client stranded.
- Show a ‘typing’ message as if a real agent is typing the reply.
- Send different types of media files: (Gifs, videos, images, and audio messages).
2. Data collecting
If you want your chatbot to convert visitors into leads and clients, you need to consider what data to collect. Ask for the data that you need, like name, email, delivery address, preferences, order parameters, and feedback. Be careful not to ask for too much sensitive data. Ensure that data is securely transferred and stored, and check the regulations in your country or state. For example, platforms that provide services to European customers, have to comply with GDPR.
3. Integration with a CRM and other software
By integrating your chatbot with a CRM, you can automatically save lead information in a single place for future use. A chatbot can recognize returning customers and start the conversation from where it left off, providing personalized messages and recommendations based on previous requests and purchases.
4. Connecting to a human agent
Non-standard requests often require a human being's involvement. Provide an option to switch to a conversation with a live agent.
5. Agent panel
If your chatbot connects with human agents, the operators should be able to view queues and inquiries, choose from predefined answers, view previous chat history, and see their own KPI. The agent panel should be easy to use and have a smooth design because people will work with it for many hours a day.
6. Admin panel
An admin panel should be used to manage chatbot parameters. This can include:
- Role management: assigning roles and permissions to other team members
- Analytics: a dashboard of key metrics and different reports to see the effectiveness of the chatbot and agents
- Notifications: managing reminders and ads
- Subscribers: viewing a list of subscribers
- Chat history: the message history
- Flow editor: editing message flow and texts.
1. Omnichannel integration
Modern brands should widen their online presence by being available on all possible customer channels, whether it be a website, mobile app, or messenger. Linking the chatbot with all these channels will ensure that all requests come to a single database and are processed in your CRM, decreasing the burden on client services.
According to CSA Research, 76% of online buyers prefer to make purchases in their native language, and 40% of shoppers refuse to buy from websites in other languages. Therefore, if you provide international services, using a multilingual chatbot is indispensable.
Platforms that provide a large variety of products can use chatbots to assist customers with their search. For example, Lidl created a sommelier chatbot that suggests the best wine based on the region, price, preferences, or composition of the meal.
Steps to Build a GPT Chatbot
Custom development is the way to go if you want a chatbot tailored to your requirements and budget. Here are the steps for building a chatbot that meets the needs of its users and provides value to your business:
- Determine the target audience, purpose, and scope of the chatbot. Who will use the chatbot, why, and what tasks will they perform?
- Choose the GPT model (or another technology) that best suits your needs based on factors such as the complexity of the chatbot, the amount of training data available, and the desired accuracy. Use the OpenAI API to interact with the model and obtain responses. Here is our article if you want to better understand the difference between GPT-3 vs. GPT-3.5-turbo vs. GPT-4.
- Prepare the training data by collecting relevant conversations or prompts that reflect the scope and purpose of the chatbot.
- Fine-tune or train the model. Once you have prepared the training data, you need to fine-tune the GPT-3 model by adjusting its hyperparameters and training it on the prepared data. Or pass an array of message objects to the model, which includes the role and content for each message, if you are using GPT-3.5-turbo. This will help the model generate more accurate and relevant responses.
- Develop the dialogue management system. The dialogue management system is responsible for handling user input and generating responses. Develop a system that can understand user input, generate appropriate responses, and manage the conversation flow.
- Design a visually appealing, easy-to-use, and consistent user interface that matches your brand. Implement the chatbot's user interface, such as a website or messaging platform.
- Integrate the chatbot with other tools or services if necessary, such as databases or APIs, to provide more functionality.
- Test the chatbot's functionality to ensure that it works as expected and generates accurate responses. Refine its responses as necessary.
- Deploy the chatbot to the chosen platform and monitor its performance.
- Continuously improve the chatbot. Analyze user feedback and make necessary adjustments to the GPT model or dialogue management system.
At Greenice, we can help you with development right from the first steps. This includes the Discovery phase, planning, choosing the right model (or other technology), and creating a prototype. And of course, we can assist with GPT integration, design, and development.
Cost to Build a GPT Chatbot
To estimate the cost of building a ChatGPT-like chatbot, there are many factors to consider, including the cost of chat development itself and the price of the model used.
When it comes to developing a chatbot, it requires a lot of planning, design, tuning/training, front-end and back-end development, and testing. You'll need a team of programmers, designers, testers, and also a Team Lead and Project Manager.
The cost of development teams can vary greatly based on the team hourly rate and the time they will spend on your project. Development time depends on team experience while the rate depends on the team's reputation and location. Prices for development teams can start from $20-40 per hour in Asia and Africa, $30-50 per hour in Latin America and Eastern Europe, $75-100 per hour in Western Europe, and $90-150 per hour in North America.
However, be aware that the team with the cheapest rate may end up being more expensive in the long run. Their product may require a lot of rework or even need to be completely redone. As for the cost of integrating GPT, the pricing varies for each language model. They are estimated in tokens, which are blocks for determining the length of a text. One thousand tokens equals approximately 750 words. This will be a recurring expense as you will pay every month for using the model. Here are the prices provided by OpenAI:
- GPT-3.5-turbo model costs $0.002 per 1,000 tokens.
- For GPT-3, the cost varies depending on the model’s complexity and ability to follow instructions, where Ada is the most basic and Davinci is the most advanced option:
- Ada costs $0.0004 for training and $0.0016 for usage per 1,000 tokens.
- Babbage costs $0.0006 for training and $0.0024 for usage per 1,000 tokens.
- Curie costs $0.0030 for training and $0.0120 for usage per 1,000 tokens.
- Davinci costs $0.0300 for training and $0.1200 for usage per 1,000 tokens.
- For GPT-4, the cost is determined by the context length:
- 8K context costs $0.03 per 1,000 prompt tokens and $0.06 per 1,000 sampled tokens.
- 32K context costs $0.06 per 1,000 prompt tokens and $0.12 per 1,000 sampled tokens.
At Greenice, we have a lot of experience developing custom solutions, including AI-powered projects and custom chatbots. We can take care of your project from idea creation to after-launch maintenance. Based on our experience, development of the chatbot will cost from $10,000 for an MVP.
Future Outlook for ChatGPT API and Chatbots
OpenAI, as well as its competitors, never stop working on new technologies. Here are just a few trends that are changing the world of AI right now:
Advanced features of GPT-4
While GPT-3 and 3.5-turbo are available for a wide public, access to GPT-4 is still limited. After it is available it will change the game of chatbot creation. For example, the image processing feature of GPT-4 opens new opportunities for chatbots. With it, bots will be able to understand more input information. For example, analyze a picture of the user's environment and offer solutions to his problem accordingly - like offering a recipe based on a photo of products in the refrigerator.
Developers are working on plugins that extend capabilities of GPT models. For example, some plugins enable ChatGPT API searching the web. This will allow bots to respond to the query with fresh data or find necessary information that wasn’t in a pre-trained dataset.
Microsoft is looking to take advantage of the hype surrounding ChatGPT in various ways. The company supplies the cloud computing infrastructure for ChatGPT. Earlier this year, Microsoft revealed that it had invested a significant amount of money in OpenAI and has been working on integrating OpenAI technologies into its own products.
Apart from incorporating ChatGPT-like technology into Bing search engine and Edge browser, Microsoft is probably planning to launch a new technology that will enable companies, schools, and governments to develop their own bots using ChatGPT. It will assist Microsoft’s clients in creating new chatbots or enhancing their existing ones. This could include providing response suggestions for call-center agents to use during customer service interactions.
Building a chatbot using GPT technologies is a game-changer for businesses of all sizes. With the ability to generate natural language responses, scale your operations, and implement your chatbot in various industries, the possibilities are endless. However, it's important to keep in mind that there are potential drawbacks such as limitations in understanding context and the cost of training and maintaining the model.
Despite these challenges, the potential benefits are worth exploring. GPT-based chatbots can improve customer service, automate repetitive tasks that drain your resources, and provide personalized recommendations to customers.
In this article, we'll outline the necessary steps to create effective ChatGPT-like chatbots that deliver engaging and helpful interactions with your customers. Alternatively, if you'd like some professional help to build the chatbot you need, we're happy to assist you.
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