You have read about generative AI and learned about some use cases that could really help your business. Some examples of business use cases include: IT support, customer support, internal HR help requests, and more.
Maybe you’ve tested ChatGPT. Or, for the more adventurous people, you’ve tried creating your own GPT in OpenAI’s GPT store. Most people find that their newly created AI “hallucinates” a lot, to the point where it is unusable (hallucination being the euphemism for making mistakes). Why is this? Because the AI you created in your GPT is trying to figure out answers to your prompts, still using the entire universe of information within OpenAI’s LLM (GPT-3.5 or GPT-4). Try as it may, it’s just too much data to sift through.
In addition, OpenAI is a bit fuzzy about keeping your data private if you put it in ChatGPT or your own GPT. If you don’t do anything, OpenAI assumes that anything you put in is theirs to use for training. That means someone can trick ChatGPT into showing someone else your information. You can opt to make your data “private”, but as of the writing of this blog, that means that OpenAI will delete your data in 30 days. It is unclear what “deletion” means in the context of a large language model.
Before you decide to integrate your data or rely on any artificial intelligence platform, including ChatGPT, it’s essential to evaluate several crucial factors. These considerations will not only help you make an informed decision but also ensure that the AI tool aligns with your needs and expectations. Here are some vital aspects to keep in mind:
1. Security
Assess the security measures implemented by the AI system to safeguard sensitive information. Look for features such as encryption, access controls, and data anonymization to minimize the risk of unauthorized access or data breaches. Ensure that the AI provider follows industry-standard security practices and has a robust security framework in place.
2. Data privacy
Understand how the AI system handles and stores your data. Review the provider’s privacy policy and terms of service to determine who owns the data, how it is used, and whether it is shared with third parties. Ensure that the AI system complies with relevant data protection regulations, such as GDPR or CCPA, depending on your jurisdiction.
3. Accuracy
Evaluate the accuracy of the AI system in providing relevant and reliable information. Test the AI with a diverse set of prompts and scenarios specific to your business domain to gauge its performance. Look for AI systems that have been trained on high-quality, domain-specific data and have mechanisms in place to handle uncertainty and ambiguity. Finally, have a human review a sample of the AI’s answers.
4. Data Ownership
Clarify data ownership rights with the AI provider. Ensure that you retain ownership and control over your proprietary data and that the AI system does not use it for purposes beyond the scope of your agreement. Look for AI providers that offer flexible data ownership options and allow you to export or delete your data if needed.
Understand the nature and quality of the training data used to develop the AI system. Ensure that the training data is relevant, diverse, and representative of your business domain. Consider AI providers that allow you to fine-tune the AI model with your own domain-specific data to improve its performance and relevance.
6. Explainability
Look for AI systems that provide transparency and explainability in their decision- making process. The AI should be able to provide clear explanations and justifications for its outputs, including linking to sources when possible, enabling you to understand how it arrived at a particular conclusion. Explainable AI helps build trust, facilitates auditing, and allows for easier troubleshooting when issues arise.
In order to effectively tackle your challenges using AI, it’s imperative to consider all of the aspects above. Unfortunately, the reality is that no major language model vendors currently offer a complete toolkit. Currently, these large language models are in the business of providing the biggest & best models, not on solving specific use cases like we are discussing here.
This highlights the necessity for a reliable partner who can deliver all the required components in an easy-to-use solution tailored to your needs. Some people call this concept “closed-loop” AI, emphasizing a system where your data is kept private and secure, preventing it from being shared or uploaded to undesirable locations.
At Hyacinth, we ensure the protection of your information while providing a customized AI solution that addresses your specific challenges. Learn more about our solution here
After addressing your challenge, it’s crucial to consider additional important factors:
1. Access : Consider where people will access your AI system. Will it be integrated into existing collaboration platforms like Slack or Microsoft Teams, or will it be accessible through a dedicated web application? Ensure that the AI system seamlessly integrates with your preferred communication channels and workflows to maximize adoption and productivity.
2. Automated Training: Evaluate whether the AI system requires manual retraining or if it can automatically learn and adapt based on new data and user interactions. Manual retraining involves periodically updating the AI model with new data and adjusting its parameters to improve its performance. This approach can be time-consuming and resource-intensive. On the other hand, an AI system that can automatically learn and adapt based on new data and user interactions can save significant time and resources in the long run. Such systems utilize machine learning techniques, such as online learning or reinforcement learning, to continuously update their models as new data becomes available. Or, the AI can have access to source training data, for example, in Confluence. This automation allows the AI system to stay up-to-date and relevant without the need for manual intervention. However, it is essential to ensure that the AI system has appropriate safeguards in place to prevent it from learning undesirable behaviors or biases from the new data.
3. Truthiness: Consider Artificial Intelligence (AI) systems with built-in scoring mechanisms that can self-validate answers. By incorporating algorithms that can assess the accuracy and credibility of the information they generate, these AI systems can provide users with a higher level of confidence in the outputs they receive. This self-validation process ensures that the AI’s responses are grounded in factual data. Moreover, the scoring mechanism allows the AI to continuously improve its performance by learning from its own mistakes and refining its knowledge base accordingly. As a result, users can trust that the information provided by such AI systems is not only relevant to their queries but also verified for accuracy, making them valuable tools for research, decision-making, and knowledge acquisition.
4. References: One great feature is the remarkable ability to provide users with direct links to videos, URLs, and other valuable resources, including original training documentation and specific page references. By providing instant access to relevant information, AI saves users time and effort in searching for reliable sources. Moreover, the inclusion of page references in training documentation allows users to quickly locate the exact information they need, enhancing the efficiency of their learning process. This seamless integration of additional resources encourages users to explore topics in greater depth, facilitating a more comprehensive understanding of the subject matter. Furthermore, by directing users to original documentation, AI ensures that the information provided is accurate, up-to-date, and trustworthy.
5. Multimodal: Evaluate if your team would benefit from the AI being able to read images. For example, if you are implementing an AI for IT support, AI systems can quickly analyze screenshots of error messages, system logs, or other visual representations of technical issues by leveraging advanced computer vision techniques. This capability streamlines the support process by eliminating the need for users to write lengthy and detailed descriptions of their problems. Instead, they can simply upload a screenshot, and the AI can automatically extract relevant information, identify the issue, and provide targeted solutions or recommendations. This approach not only saves time for both the user and the support team but also reduces the potential for misunderstandings or miscommunications that can arise from written descriptions. Furthermore, AI-powered image analysis can help detect patterns and trends in support requests, enabling proactive identification and resolution of common issues.
Conclusion
After identifying the specific use case(s) for artificial intelligence that best suits your needs, it’s essential to delve deeper into the myriad of considerations that come into play before implementing an AI solution. This is where partnering with Hyacinth can make a significant difference. Hyacinth specializes in tackling these challenges head-on, offering a solution that not only meets your needs but also prioritizes safety, security, and accuracy. Our expertise ensures that you’re not just deploying a chatbot, but you’re implementing a comprehensive solution tailored to navigate the complexities of AI integration effectively. Read more about how Hyacinth solved IT Support challenges for a customer here. With Hyacinth, you gain the assurance of a reliable AI partner, who addresses the nuances of your needs while safeguarding your data and ensuring the highest levels of precision.