AIF-C01 Question 116
Single answerA company wants to build a customer support chatbot using a foundation model. They are evaluating different customization approaches to minimize costs while achieving acceptable performance. Which approach would likely have the lowest overall cost if the company requires the chatbot to handle domain-specific queries but does not have extensive machine learning expertise?
- A
Pre-training a new foundation model from scratch using their domain-specific data
- B
Fine-tuning an existing foundation model on a small set of domain-specific data
- C
Using in-context learning with a pre-trained foundation model by providing domain-specific examples as part of the input prompt
- D
Implementing a Retrieval-Augmented Generation (RAG) solution that combines the foundation model with an external knowledge datastore
Show answer and explanation
Correct answer: C
Explanation
In-context learning is the most cost-effective customization approach for the scenario described because it eliminates the need for training or fine-tuning the model. Instead, domain-specific examples can be provided directly in the input prompt to guide the model’s behavior. This approach is ideal for companies that lack extensive machine learning expertise and want to minimize costs while still achieving acceptable performance for domain-specific tasks. Other approaches, such as pre-training, fine-tuning, or RAG, involve greater costs or technical complexity.
- A. Incorrect.
Pre-training a new foundation model from scratch is extremely resource-intensive, requiring significant computational resources and expertise. This approach would have the highest cost and is not suitable for companies without extensive ML expertise.
- B. Incorrect.
Fine-tuning an existing foundation model can reduce costs compared to pre-training but still requires some machine learning expertise and access to computational resources for training. While less expensive than pre-training, this is not the lowest-cost option.
- C. Correct.
In-context learning allows the use of a pre-trained foundation model without requiring model retraining. By providing domain-specific examples in the input, the company avoids the need for training infrastructure, making this the lowest-cost option that still achieves acceptable performance for many use cases.
- D. Incorrect.
A Retrieval-Augmented Generation (RAG) approach can improve domain-specific performance by combining an external knowledge store with a foundation model, but setting up and maintaining the knowledge datastore can involve additional costs and technical complexity compared to in-context learning.