Due to widespread concerns about generative AI, explaining how these tools work has a much higher threshold than most solutions. Users not only want to know what generative AI can do but also how it works. Therefore, establishing trust by ensuring model accuracy and making answers easy to verify is crucial, requiring additional time and investment.
For instance, an insurance company created a generative AI tool to help manage claims. As part of the tool, it listed all the safeguards in place and provided sentence or page links to relevant policy documents for each answer. The company also used LLM to generate many variations of the same question to ensure answer consistency. These steps are critical for helping end-users build trust in the tool.
Enhancing the Reusability of Generative AI Tools
Training sessions for maintenance teams using generative AI tools should help them understand the model's limitations and how to obtain the correct answers most effectively. This includes teaching workers strategies to find the best answer quickly by starting with broad questions and then narrowing down. This approach provides the model with more context and helps eliminate biases from those who might think they already know the answer. Having a model interface that looks and feels the same as existing tools also helps users avoid feeling overwhelmed when new applications are introduced.
The key to scaling is that enterprises need to stop building single-use solutions that are difficult to apply to other similar use cases. For example, a global energy and materials company has established reusability as a key requirement for all its AI models, finding that 50% to 60% of their components could be reused in early iterations. This means setting standards for developing general AI assets, such as prompts and contexts, so they can be easily reused in other situations.
Addressing Generative AI Risks
Many of the risk issues associated with generative AI are evolutions of existing discussions, such as data privacy, security, bias risks, job replacement, and intellectual property protection. However, generative AI greatly expands the scope of these risks. Only 21% of companies reporting AI adoption say they have established policies regulating employee use of generative AI technologies.
A set of test suites for AI/generative AI solutions should be established to demonstrate respect for data privacy, debiasing, and intellectual property protection. Some organizations are proposing to release models with detailed performance characteristic documentation. Documenting your decisions and reasoning is especially helpful in conversations with regulators.
Using HaxiTAG Solutions to Build Innovation Value in Your AI Applications
HaxiTAG’s data intelligence components provide efficient human-machine interaction to verify facts, automatically check data accuracy, and achieve various operational goals. It helps business partners with data modeling of digital assets and production elements, offering robust business support that significantly improves management efficiency, decision quality, and productivity. By creating innovation value models and enhancing operational efficiency, HaxiTAG enhances corporate competitiveness.
As a trusted LLM and GenAI industry application solution, HaxiTAG offers enterprises LLM and GenAI application solutions, private AI, and robotic process automation to improve the efficiency and productivity of applications and production systems. It helps partners leverage their data knowledge assets, integrate heterogeneous multimodal information, and combine advanced AI capabilities to support fintech and enterprise application scenarios, creating value and growth opportunities.
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