What is AI Model Adaptation, and Why Does It Matter for your AI application ?
AI Model Adaptation - The process of tweaking foundation models to suit your application
In this video, I explore a critical concept in AI application development: AI Model Adaptation. But what exactly does it mean, and how does it impact the way we build AI applications?
Types of AI Applications:
End-to-End AI Applications: Built entirely in-house, these applications offer tight integration but demand significant resources—time, money, and expertise.
Foundation Model led AI Applications: With the rise of foundation models like GPT-4o, companies like OpenAI and Google have democratized AI by offering these models through APIs, allowing instant access and faster time-to-market.
Challenges with Foundation Model led AI applications:
Applications have less control over the model
Model is typically designed for generic use cases.
Scaling AI application can lead to rapidly increasing costs.
The Solution: AI Model Adaptation
To tailor these foundation models to your specific needs, you need to tweak or adapt the AI Model to suit your use case. This process called “AI Model adaptation” ensures that the AI aligns with your unique use case, providing the flexibility needed to overcome the limitations of generic models.
Popular AI Model Adaptation Methods:
Prompt Engineering
Retrieval Augmented Generation
Parameter Efficient Fine-Tuning
Instruction Fine-Tuning