 Problem Context : The task is to build an LLM-based question-answering application that integrates new documents frequently with minimal costs and development efforts.

 Explanation of Options :

Option A : Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing to frequently retrain the model. This method offers a balance of cost-effectiveness and functionality.

Option B : Requires frequent retraining of the LLM, which is costly and labor-intensive.

Option C : Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it’s part of an ongoing retraining or updating mechanism, which would increase costs.

Option D : Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.

Option A is the most suitable as it provides a cost-effective, minimal development approach while ensuring the application remains up-to-date with new information.