Which two issues are MOST likely to reduce answer quality in a RAG pipeline?

In a Retrieval-Augmented Generation (RAG) pipeline, the quality of the final answer is highly dependent on the quality of the context provided to the LLM. A poor chunking strategy breaks documents into irrelevant or incomplete pieces, leading to noisy and unhelpful context. Similarly, low-quality embeddings fail to capture the semantic meaning of the text, causing the retrieval system to fetch irrelevant documents (retrieval mismatch). If the retrieved context is poor, the LLM cannot generate a high-quality, accurate answer, regardless of its own capabilities.

C. Using citations is a feature that improves answer quality and trustworthiness by providing verifiable sources, not a cause of reduction.

D. Capturing metadata (e.g., document source, date) typically enhances RAG pipelines by enabling more precise filtering and retrieval.

E. Source filtering is a technique used to narrow the search to relevant documents, which generally improves answer quality by reducing noise.

1. Gao, Y., et al. (2023). "Retrieval-Augmented Generation for Large Language Models: A Survey." This survey extensively covers the RAG pipeline. Section 3, "Core Components of RAG," details the importance of the retriever, where chunking and embedding quality are critical factors for performance. (Available on arXiv:2312.10997).

2. NVIDIA AI Enterprise Documentation, "NVIDIA RAG LLM Operator." The documentation for building RAG applications emphasizes best practices for data preprocessing, including document chunking and the selection of high-quality embedding models as foundational steps for a successful implementation.

3. Ma, X., et al. (2023). "Query Rewriting for Retrieval-Augmented Large Language Models." This paper investigates RAG failure points, noting that mismatches between query and document embeddings are a primary cause of poor retrieval, which directly impacts final answer quality. (Available on arXiv:2305.14283).