ChatGPT is a language model developed by OpenAI, not a substitute for conversational AI platforms. It can be used as a component in building conversational AI systems but is not a standalone platform for building chatbots or conversational AI applications. A Large Language Model (LLM) is used to develop ChatGPT. LLMs are artificial intelligence tools capable of reading, summarizing, and translating texts that foretell the words that will come next in a sentence. ChatGPT is not connected to the internet but uses many pre-2021 historical and present data. Its limited capabilities may help you in multiple day-to-day activities, but it is not a replacement for Conversational AI technology.
Let’s explore the contrasts between these two technological advancements and how organizations might use them to complement one another.
Differences: ChatGPT and Conversational AI
Platforms like ChatGPT and conversational AI are not directly interchangeable because of their differing designs. Conversational AI is a rapidly growing field, with the development of new technologies and applications in chatbots, virtual assistants, and voice-based interfaces. One of the critical components of a conversational AI system is the language model, which is responsible for generating human-like responses based on the input received. These platforms often provide various tools and features required for any virtual assistant to operate efficiently, including bot life cycle management, machine learning models, natural language processing (NLP) algorithms, dialogue management systems, and integrated user interfaces.
ChatGPT, on the other hand, is a machine-learning model created to produce text that resembles a human based on a given prompt or environment. OpenAI’s ChatGPT is a state-of-the-art language model trained on massive text data and can generate responses in natural language. It has become one of the most popular language models for building conversational AI applications due to its impressive performance and ease of use. While ChatGPT can generate responses in natural language, it cannot perform tasks such as understanding the context of a conversation, recognizing entities, and performing actions. These are crucial elements of a conversational AI system that require additional components to the platform.
How does ChatGPT complement Conversational AI Platforms?
Let’s see how ChatGPT Compliments Conversational AI Platforms like Kore.ai, yellow.ai, Uniphore, etc. Integration of LLMs aspects can maximize the potential of conversational AI platforms such as Kor.ai, yellow.ai, Uniphore, etc., in various areas and expedite bot development. To illustrate, some of these features include:
- Identification and automatic generation of answers are made feasible by integration with models like Open AI. It can automatically respond to frequently asked questions from PDF documents without requiring their extraction or training of the Knowledge Graph.
- Automatic Intent Recognition: By analyzing the language and semantic meaning of the utterance using massive language models that have already undergone training, virtual assistants may detect the correct intent automatically. There is no requirement that you offer any training utterances.
- Better Test Data Generation: We can automatically generate enormous volumes of test data that will continue to enhance human-bot interactions in a fraction of the time using massive language models.
- Slot and Entity Identification: By automatically producing slots and entities for particular use cases, we can employ LLMs to accelerate dialogue development. As a result, a developer can analyze and wire the integration with their backend more quickly and follow enterprise business requirements.
- Generate Prompts and Messages: LLMs can significantly reduce training time by creating preliminary prompts and messages displayed to the end user during training. Copywriters can update human-like communications to ensure adherence to enterprise-specific standards in responses rather than having to create everything fresh.
- Create Sample Conversations: LLMs may build whole sample conversations between a bot and a user for any use case, providing Conversation Designers with a great place to start when fine-tuning responses.
Important ChatGPT’s drawbacks as an Intelligent Virtual Assistant:
- Integrations for Transactional Tasks Are Missing: Most clients must communicate with the Agent for more complicated transactions that call for integrations between their backend systems.
- Does not offer factually accurate information: There is no way to guarantee that LLM and ChatGPT models’ responses are always factually accurate, even while they can produce solutions that might suit specific situations. Large datasets generate text by LLMs and ChatGPT. Still, because they lack access to outside information or a grasp of the real world, their output is frequently constrained, resulting in solutions based on data that is inaccurate, out-of-date, or inappropriate for the situation. ChatGPT “sometimes writes plausible-sounding but wrong or nonsensical answers,” according to OpenAI. A skilled copywriter may customize each response a virtual assistant’s response provides by utilizing conversational AI, ensuring that users have smooth and accurate discussions.
- Unable to resolve issues unique to an enterprise: These models can produce responses that might be suitable in various situations, but they cannot respond to questions or FAQs that are distinctive to a particular company. A chatbot or virtual assistant powered by conversational AI can be trained on specific datasets and integrate with your company’s existing systems and processes, producing relevant questions and replies for customers; this is what you will need for this specificity to happen.
- Data security and privacy are issues: Virtual assistants manage a sizable quantity of sensitive data, and many conversational AI platforms prioritize security and include the necessary capabilities to ensure it. However, there are several security issues to take into account while using LLMs with ChatGPT, having:
- LLMs and other NLP models may process and analyze personal data, such as names, addresses, and further identifiable details. It may also process sensitive or confidential data, like financial or medical information. These models might handle data available to or shared with third parties, such as partners or service providers. Taking proper security measures to safeguard this data from unauthorized access or exposure is crucial.
- These models may generate or store significant volumes of data. In addition to ensuring that data is saved and managed in a manner that complies with applicable data protection laws and regulations, it is crucial to have a clear and transparent data retention strategy in place.
ChatGPT solves only a portion of conversational AI problems. When it comes to offering the tools and capabilities required for enterprise-grade users, ChatGPT, like other LLMs, falls short. IGT helps Businesses and organizations maintain an end-to-end lifecycle, including creating, training, testing, and deploying IVAs that can engage with clients and users. They can be engaged in a natural and human-like manner, leveraging conversational AI platforms. These platforms often offer tools and frameworks for creating, developing, and deploying chatbots. They also frequently provide APIs and other choices for integrating chatbots with other communication channels, including websites, messaging apps, and voice assistants.
Overall, ChatGPT is a robust tool that enhances the effectiveness and quality of chatbot dialogues. Still, it is only one component of a conversational AI platform that may be developed and deployed. LLMs are ideal for those who want to practice snappy retorts or test pop culture references. Customers still need quick and efficient problem-solving, which is why AI-driven virtual assistants stand out as brilliant customer service solutions.
Ramani Giri is the Senior Business Analyst at IGT Solutions’ Intelligent Automation Team. She has an overall 3 years of experience in the Travel and Hospitality domain. With expertise in Data Analytics, Process Automation, and Conversational AI for delivering back-office automation solutions. Outside of work, Ramani likes to swim, travel and read.