The most cost-effective and scalable solution is to use a Knowledge Base for Amazon Bedrock. This feature is specifically designed for Retrieval-Augmented Generation (RAG), a technique ideal for this scenario. It works by ingesting the PDF documents, converting them into a searchable vector format, and storing them. At query time, the system retrieves only the most relevant snippets of information from the manuals to augment the user's prompt. This keeps the input to the Large Language Model (LLM) concise and relevant, which significantly reduces the number of tokens processed per query and, therefore, minimizes inference costs. This method avoids the high costs associated with fine-tuning or sending large, irrelevant context in every prompt.

1. Official Vendor Documentation: AWS Documentation

"Knowledge bases for Amazon Bedrock." The documentation states

"Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you implement the entire RAG workflow... without having to build custom integrations to data sources." This directly describes the solution in option D.

2. Official Vendor Documentation: AWS Machine Learning Blog

"Choose the right model customization method in Amazon Bedrock." In the section comparing RAG and fine-tuning

it notes

"RAG can be more cost-effective than fine-tuning

especially for models that are used infrequently... RAG is a good choice when your model needs to access rapidly changing information." This supports the cost-effectiveness of the RAG approach (implemented by Knowledge Bases) over fine-tuning (Option C).

3. Peer-Reviewed Academic Publication: Lewis

P.

et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Advances in Neural Information Processing Systems 33

Section 1

Paragraph 2. This foundational paper introduces the RAG framework

which combines a pre-trained retriever with a pre-trained generator. The Knowledge Base in Bedrock is a managed implementation of this exact

efficient pattern. (Available via arXiv:2005.11401)

4. University Courseware: Stanford University

CS224N - NLP with Deep Learning

Winter 2023

Lecture 17

"Retrieval and Question Answering." The lecture materials discuss RAG as a state-of-the-art method for open-domain question answering

highlighting its ability to ground models in external knowledge without costly retraining

which aligns with the cost-effectiveness requirement.