Hamid

When researching these approaches, one needs to consider a few factors like complexity, data, performance, and time to develop and deploy. Each approach has its own strength and weaknesses, and one need to be discerning when reviewing the goals of the AI project. Below are some advantages and limitations per approach.
 
Further below is a table with some considerations and a quick view to determine which approach would be suitable for your potential use case.
 
AI Solution Approaches: Comparison and Contrast
 
1. Conventional AI Models and Methods
- Advantages:
    -. This method is generally understood and well established.
    - Uses less CPU power generally.
- Limitations:
    - May not handle complex tasks.
    - Requires lots of specific expertise.
 
2. Fine-Tuning Existing LLMs
- Advantages:
    - Uses existing knowledge and current capabilities.
    - Quicker to deploy.
    - Able to deliver a high performance.
- Limitations:
    - Performance can be inconsistent.
 3. Training a New AI Model from Scratch
-Advantages:
    - Can be set up to specific needs.
    - Can be better in performance.
- Limitations:
    - Resources can be expensive based on CPU power requirements.
    - Needs large datasets.
 
4. Designing Solutions with Flow and Prompt Engineering
- Advantages:
    - To develop this can be done quick and updated too.
    - Can make use of LLMs and avert high costs.
- Limitations:
    - This needs higher expertise in prompt engineering.
 
 

The most relevant and practical scenario that comes to mind is employing an AI Chabot which is capable of handling Customer Support/Service/General enquiries.
 
The flow of the chat bot should be as follows:
1.      Direct customers to self-help information and FAQ's.
2.      Guide on troubleshooting and
3.      Escalate or route to a specialized/complex/complaints dept. operated by humans to further intervene and resolve.
 
One such scenario is when the AI chatbot is dealing with Gas and power (Energy Utilities) customers that have requested an invoice to be rebilled to an accurate read as it constantly bills to an estimated read resulting in an inaccurate invoice.
 
Post all the rules of elimination via troubleshooting and there is still no satisfaction, the AI chatbot will need to route the error/issue together with the interaction and troubleshooting history to a technical dept./Human so that a physical inspection can be conducted on site to establish if there is any physical impairment or issue with the physical meter.
 
Thereafter a physical meter fix/replacement will need to be actioned, and the human agent can then post their updates and fixes to the customer and then hand back to an AI chatbot once the client/end customer has confirmed they are satisfied with the rebilling to an accurate bill/invoice.