Explanation:
Option A is correct because it describes a Retrieval-Augmented Generation (RAG) approach using a retriever, prompt, and LLM. This architecture efficiently incorporates new documents by retrieving relevant content and inserting it into the prompt, minimizing costs and development effort compared to fine-tuning or frequent LLM updates. Option B is incorrect as it suggests frequent LLM updates, which is costly and inefficient for handling new documents. Option C is insufficient as it lacks a retrieval mechanism for incorporating new documents. Option D is suboptimal because using an agent and fine-tuned LLM increases complexity, cost, and development effort, making it less suitable for the requirements.
A Generative AI Engineer needs to build a low-cost, low-effort LLM-based question-answering application that incorporates frequently published new documents. Which combination of chaining components and configuration satisfies these requirements?
A
For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
B
The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
C
For the question-answering application, prompt engineering and an LLM are required to generate answers.
D
For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.
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