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The GPT Conundrum: Challenges for businesses to adopt Generative AI
Generative AI: From Prediction to Creation
The history of artificial intelligence (AI) in the technology industry traces back to 19th century, when Alan Turing, proposed the idea of machine intelligence as the ability of a theoretical device to execute any computer algorithm, no matter how complex the problem is, with enough resources. This inspired modern AI research that is based on the core principle of explicit programming (a set of rules and algorithms were specified to perform a specific set of tasks).

Website chatbots or device intelligence assistants (Siri, Bixby, Alexa) are industrial products that can follow predetermined patterns to respond to user queries. The intelligence is coupled with the ability of the machine to outperform humans in taxing tasks in terms of accuracy and consistency, as long as all use cases are properly outlined.

However, this also exposes the inflexibility of the machine to adapt to new or dynamic situations, and it can only be overcome by updating its programming logic from time to time, which makes the solution impractical and inefficient in the long run. Instead of a fixed set of algorithms, a more agile system that can emulate or even surpass the intelligence and the logical reasoning of humans is more favorable, which leads us to Generative AI which operates on another level of paradigm.
Illustration by Carla Zucchi
Illustration by Carla Zucchi
Generative AI is more than just forecasting and prediction, its key strength features the generation of output autonomously through fine-tuning the model using vast amount of training data. The output generation isn’t limited to only text, but also visuals, audio, and video, which enables product sensations like ChatGPT and Dall-E. These generative capabilities are not new, but they have been enhanced by the recent progress in technology and data availability.

For instance, ChatGPT uses generative pre-trained transformers (GPT), which are a significant improvement over the older technique of Markov chains, introduced in 1906, that could only predict the next word but not generate meaningful and complete sentences. The open-source community also contributes to the trend by developing more GPT variants that are trained on different large data sets, such as LaMDA, PaLM, and LLAMA, which are fine-tuned for different domains and applications. Therefore, instead of following a set of rules, prompt engineering is the key skill to guide the model’s output.
However, the beauty of the innovation lies in how we can link multiple prompts together to tackle a more complex or multidimensional problem. Rather than a simple one-to-one output generation, GPT can now act as an agent that orchestrates the entire process of how a human would approach a problem, from the thinking to the execution, to the final output, based on the instructions provided. All this is made possible by creating multiple small tools and connecting them with a robust framework like LangChain. Some of the notable examples include autonomous agents like AutoGPT, copilot tools like GitHub Copilot, and search engines like Microsoft Bing Copilot.
Generative AI: Navigating through common pitfalls
“With great power there must also come great responsibility”. Generative AI is not without its flaws and challenges. Managing generative AI in the corporate business context will always be a challenge since we want to maintain a healthy balance on Solutions that are built based on this technology need to ensure the quality and reliability of the outputs, as well as the satisfaction and trust of the users. Moreover, generative AI is still an evolving technology, and managing its rapid development and innovation is tedious but crucial to avoid causing disruption and harm to the businesses and end users.
We often receive clients inquiries questioning about the reliability of AI solutions. “How safe is it?”, “We don’t have the budget and infrastructure to set up the solution!”, “How can this be useful for us?”. Let’s address them once and for all!
(A) AI safety and governance
When businesses adopt new tools or technologies, security and governance form the core pillars that support the trust and reputation of any organization. Failing to address these issues can lead to serious risks such as data breaches, ethical dilemmas, and cyber fraud, affecting both internal and external stakeholders.

ChatGPT is powered by the state-of-the-art model, GPT4 that can generate natural and engaging responses based on a given context. However, it is not perfect and can sometimes produce inaccurate or nonsensical answers. This is because the model is trained on a large corpus of public data that may not reflect the latest facts or the specific domain knowledge. Conceptually, GPT4 is based on Reinforcement Learning from Human Feedback (RLHF), which means human feedback acts as a performance measure to optimize the model, such that it can contextually generate appropriate responses that align more closely with human expectations. By incorporating user feedback mechanism (thumbs up / down), this helps OpenAI team to evaluate the accuracy or perform fine tuning, which explains why there will be updated models being released from time to time (at least every 3 months). However, since these improvements are not real-time and it mainly benefits the product in the long run, they still include disclaimers as such to urge users to verify the authenticity of certain responses when accuracy matters the most.
Disclaimer on ChatGPT interface
Disclaimer on ChatGPT interface

Businesses that employ GPT models also need to consider the implications on their intellectual property (IP) and sensitive data. For example, a chatbot that is deployed on their website may expose their proprietary information to their competitors or malicious actors. In terms of data sensitivity, most ready-made and callable Large Language Model (LLM) API specify that any sensitive user input will not be part of the training data unless a custom fine-tuned model approach is used.

As for IP, it depends on the solution architecture. For XYAN, Retrieval Augmentation Generation (RAG) approach is used to ground the chatbot with its website content that is already publicly available. This way, the chatbot does not reveal any information that is not already accessible by other means (Google crawling engine will definitely have access to any public website information which powers AI search engine, like Bing, which any personnel can easily access them). However, this also means that businesses need to stand out from their competitors by offering unique and valuable products and services, as well as delivering them in a satisfying and effective way. This is the core of the business strategy that goes beyond chatbot technology and what the fellow business stakeholders should ponder on instead of worrying about the public availability of their website information.
Disclaimer by Azure OpenAI service on data governance and security
Disclaimer by Azure OpenAI service on data governance and security
(B) AI Accessibility
Artificial Intelligence (AI) is deemed to be influential and able to revolutionize various sectors and fields. Many tech companies have aspired to create AI products, but they often encountered difficulties and hurdles at different phases of the development process, such as setting up the environment, training and fine-tuning the data, and deploying the models.

Learning Python programming language is a start, but to build a sophisticated solution that adds value to the business or organization often demands more than that. It typically involves key components like data, the technology that drives the solution and the methodology that determines the solution outcome. Fast forward to this date, there has been remarkable progress across all these aspects which makes AI solution development much easier than before.
Big Data

Data is the essential component of any machine learning project, and its quality has a significant impact on the training process, fine tuning strategy and the prediction or generation of the response, especially in the field of Natural Language Processing (NLP). In the past, we have witnessed how tech giants like Google have utilized the reCAPTCHA feature to obtain image data annotations from people around the world, which greatly enhances object detection capabilities.

Over the years, many applications, software, websites, or even IoT devices have become more data driven, which increases data availability and diversity. With the advent of transformer models, unstructured data has also gained prominence, which implies that the internet has become the largest data repository where millions of websites are hosted.

Since the models can comprehend data in a more sophisticated way through text embeddings, the website itself can power the chatbot technology without requiring a separate knowledge base. When the data is well-managed, it can benefit the businesses immensely by bridging the gap between the internal stakeholder and front-end consumer, which helps them to make more sound business decisions. Business goals will then be adjusted to allow more AI integrations which can empower users in a more personalized experience.
How data plays a role in making AI accessible for everyone
How data plays a role in making AI accessible for everyone
Cloud-based services

Cloud computing is the other aspect that complements AI very well. It provides the computing resources and infrastructure needed to train and deploy AI models at a scale. Cloud services providers like Microsoft Azure have been a boon to the developer community since their OpenAI collaboration announcement. With Azure OpenAI services, developers can access a wide range of AI models, including natural language processing, computer vision, and even an orchestrator to add mini multiples tools to their applications.

By simply signing up and adding an API key to the code base, developers can easily replicate the main functionality of ChatGPT and access AI services on demand, without having to invest in expensive hardware and software. Users can also scale up or down their computing resources according to their needs, and only pay for what they use, with all the security and governance regulations in place. Learning and building AI solutions has never been more convenient!
How cloud-based framework plays a part in revolutionizing AI services
How cloud-based framework plays a part in revolutionizing AI services
Democratizing AI

AI and programming itself are often daunting for non-technical staff and management personnels. While learning a new skill at work is beneficial, it can be challenging to meet tight deadlines for client projects. Having a team of technical experts is essential, but it is also desirable to have the decision maker participate in the development process to some extent. This became feasible when prompt engineering emerged after the launch of ChatGPT.

Working with Large Language Models (LLM) requires not only programming knowledge, but also the ability to understand the requirements and translate them into constructive prompts or meaningful instructions. Writing prompts can significantly reduce the time and effort spent on debugging and testing the code, compared to using pure programming syntax.

Prompt engineering can enhance the efficiency and versatility of AI models, by minimizing the training time and expanding the scope of tasks they can perform. However, prompt engineering is not a substitute for coding, but rather a supplementary method that make good use of the enormous potential of Generative AI. With the time saved, developers can concentrate more on complex aspects such as system analysis or design optimization.
Democratizing AI for businesses, developer and front-end user
Democratizing AI for businesses, developer and front-end user

Democratizing AI for businesses, developer and front-end user

XYAN: Pioneering new way to work with your content
With XYAN, our generative AI platform, you can transform your business with customized agents and tools that automate your daily operations and workflows. Internally, team members can interact with the AI features as if you are talking to a personal assistant. You will get professional and accurate responses that guide you to the next steps, making your experience smooth and easy. You will also free up your time and energy from repetitive tasks, and focus more on creative and strategic work that adds more value. Front end consumer can easily navigate through your website or product listing to obtain insightful information whereby the AI feature can easily generate without any hardcoded QnA.

But that’s not all. XYAN is not just another chatbot or generative agent that you can create with the resources that are publicly available. XYAN empowers any businesses to get started by seamlessly integrates to any website (built on XTOPIA or other CMS platform) after crawling the existing website pages, and creating a knowledgebase based on all the website content or documents within hours. XYAN is not just a typical search engine, but it can empower chatbot to generate responses based on the user’s query and behaviour (past browsing history and interaction). The generated responses are not just plain text, but dynamic pages with rich media objects (image, videos) in various layouts that the website admin can customize. And the best part is, the knowledgebases are constantly updated to reflect any changes on the live site, making the maintenance process as effortless as possible.

XYAN is the ultimate generative AI platform that will give you the edge over your competitors. XYAN is the key difference that will make your business stand out in your industry. Don’t miss this opportunity to join the generative AI revolution. Contact us today and get started with XYAN.

XIMNET is a digital solutions provider with two decades of track records specialising in web application development, AI Chatbot and system integration. XIMNET is launching a brand new way of building AI Chatbot with XYAN. Get in touch with us to find out more.
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