This scenario describes the ideal use case for Retrieval-Augmented Generation (RAG). The RAG architecture, as outlined in option A, combines a retriever with a Large Language Model (LLM). The retriever searches an external knowledge base (e.g., a vector index of the new documents) for relevant context based on the user's query. This context is then inserted into the prompt and passed to the LLM. This approach directly addresses the need to incorporate frequently updated documents without the high cost and development effort of repeatedly fine-tuning the entire model. Updating the vector index is significantly cheaper and faster than retraining an LLM.

B. Frequently fine-tuning an LLM is extremely expensive in terms of computation and time, directly contradicting the requirement for the lowest possible cost.

C. Using only prompt engineering and an LLM provides no mechanism to access the new documents; the LLM's knowledge is limited to its original training data.

D. This option is overly complex and costly. It includes fine-tuning (expensive) and an agent (more complex than a simple retriever), violating the "least cost and least development effort" constraints.

1. Databricks Documentation, "Introduction to Retrieval-Augmented Generation (RAG)": "RAG is a design pattern that can improve the quality of LLM output by providing the model with relevant, specific information from an external knowledge source. This helps the model to produce more accurate, up-to-date, and relevant responses... RAG is a more cost-effective approach than fine-tuning a model for every domain-specific task." This directly supports option A as the cost-effective solution for incorporating up-to-date information.

Source: Databricks AI and Machine Learning Documentation > Generative AI > RAG.

2. Databricks Blog, "What is Retrieval-Augmented Generation (RAG)?": "Fine-tuning is a great option for adapting an LLM to a particular style or to learn a new skill, but it is not as effective for knowledge injection... RAG allows LLMs to access and utilize up-to-date information without the need for constant retraining, making it a more scalable and cost-effective approach." This explains why RAG (Option A) is superior to fine-tuning (Option B) for this specific use case.

Source: Databricks Blog, AI & Machine Learning section, Published August 24, 2023.

3. Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Advances in Neural Information Processing Systems 33.: The foundational academic paper on RAG introduces it as a method where the parametric memory of a pre-trained model is augmented with a non-parametric memory (the retriever). The abstract states, "Our method can be fine-tuned end-to-end, and allows the retriever and generator to be trained jointly," but its primary advantage for this scenario is the ability to update the knowledge base without retraining the entire model.

Source: Section 1 (Introduction), which contrasts the RAG approach with models that store all knowledge in their parameters, highlighting RAG's ability to easily update its knowledge source. (Available on arXiv:2005.11401 and at NeurIPS proceedings).