Option C is correct because the issue is retrieval quality: responses contain irrelevant information and sometimes miss important surrounding context. Amazon Bedrock Knowledge Bases supports hierarchical chunking with parent and child chunks. In this pattern, smaller child chunks improve precise retrieval, while larger parent chunks provide broader surrounding context when returned for generation. AWS documentation states that hierarchical chunking supports two levels: a parent chunk size and a child chunk size, with configurable overlap. This directly addresses the textbook problem because dense technical materials often need both fine-grained matching and enough context to avoid incomplete or misleading answers.
Hybrid search is also the strongest retrieval option for domain-specific textbooks. AWS documentation for Knowledge Bases explains that HYBRID search uses both vector embeddings and raw text, while SEMANTIC search uses only vector embeddings. For technical content, equations, terms, acronyms, product names, and exact phrases may be missed by pure semantic search. Hybrid search reduces this risk by combining semantic similarity with keyword matching. The Knowledge Base vector search configuration also supports controls such as number of source chunks, metadata filters, search type override, and reranking configuration, all of which help tune retrieval quality.
Adjusting the relevance threshold, or filtering low-confidence retrieval results in the application layer, helps reduce hallucinations because the model should not generate answers from weak or unrelated context. The key GenAI principle is that RAG quality depends on retrieving the right evidence before generation. If irrelevant chunks are passed to the FM, the FM can produce irrelevant or unsupported answers.
Option A uses very small fixed chunks, which can miss context across sections. Option B adds caching and feedback loops but does not fix the underlying retrieval issue. Option D’s semantic chunking can help topic boundaries, but it does not explicitly solve exact-term retrieval or low-confidence filtering. Therefore, hierarchical chunking plus hybrid search and relevance filtering is the best answer.
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