Readiness
A Worthy Problem
Do you have a right problem?
Before thinking about the technology, you should work on the problem.
The problem should ideally have at least one or two of the following attributes:
- Importance
- Severity
- Cost
- Urgency
- Frequency
- Growth
Maximize Generative AI with Strategic Use Cases
You need good use cases that could maximise the power of Generative AI models.
It’s essential to identify use cases that fully leverage the capabilities of Generative AI (GA), such as large language models (LLMs).
Regardless of the specific task, the use cases should demonstrate how these models can deliver substantial value by improving efficiency, increasing productivity, reducing costs, or enhancing customer satisfaction. Furthermore, these use cases illustrate how the implementation of GA models can lead to disruptive changes within a sector.
Data: The Queen of AI Analytics
If IC design remains the king of computing systems in the era of AI, data is the queen of AI applications.
A sufficient amount of high-quality data is a critical building block for any AI Agent application.
A business must have an effective data management framework and mechanism in place to build an AI Agent application that works as expected.
A Strong Test Plan is Key
As a best practice in software engineering, a test plan is required before development.
This principle is even more important in AI Agent development.
Though the capability of LLMs has reached a very high level, we still don’t have full certainty or control over their behaviour.
Testing is the most effective method to ensure LLMs perform their assigned roles as designed and function as expected.
Create a robust test plan before development and maintain ongoing testing throughout product lifecycle.
Being prepared ensures the success of your AI Agent innovation.
In short, start your AI journey with the right problem, practical use cases, quality data, and a solid test plan.
