SnowPro Specialty: Gen AI Question 193
Single answerData transformationsA team is building a retrieval-augmented generation (RAG) solution in Snowflake over a large set of product manuals stored as raw text. Early testing shows poor answer quality because chunks frequently split tables, headings, and adjacent paragraphs, causing embeddings to miss important context. The team wants to improve retrieval relevance before regenerating embeddings. Which transformation is the most appropriate to apply first?
- A
Replace semantic chunking with fixed-size character chunking so every chunk has identical length
- B
Transform the documents into structure-aware chunks that preserve logical sections such as headings, lists, and table boundaries before creating embeddings
- C
Remove punctuation and stop words from the manuals before chunking so the embeddings focus only on key terms
- D
Convert all manuals to uppercase before generating embeddings so tokenization is standardized across documents
Show answer and explanation
Correct answer: B
Explanation
The core problem is not the embedding model but the quality of the input transformation before embeddings are generated. In RAG systems, chunking strategy strongly affects retrieval quality because embeddings work best when each chunk represents a coherent semantic unit. For documents like manuals, preserving document structure during transformation is a best practice: headings should stay associated with their content, lists should remain intact, and tables should not be split arbitrarily when possible. Snowflake Gen AI workflows commonly rely on preparing high-quality text segments before embedding and retrieval, and this aligns with broader best practices for document preprocessing in vector search pipelines. By fixing chunk quality first, the team improves grounding and relevance before spending time recomputing embeddings.
- A. Incorrect.
This is incorrect. Fixed-size character chunking is simple, but it often breaks meaning across arbitrary boundaries and can split related content like section titles from their body text. In a RAG pipeline, equal-length chunks do not guarantee semantically coherent units, so this usually worsens retrieval for technical manuals rather than improving it.
- B. Correct.
This is correct. For technical manuals, structure-aware transformation is the best first step because it preserves the semantic organization of the source, such as headings, bullet lists, and tables. Creating embeddings from coherent, logically grouped chunks usually improves similarity search and downstream answer grounding. This is a practical transformation choice when retrieval issues are caused by poor chunk boundaries rather than the embedding model itself.
- C. Incorrect.
This is incorrect. Removing punctuation and stop words is a legacy NLP preprocessing pattern that is generally not appropriate for modern embedding workflows. Punctuation, formatting cues, and common words can carry meaning, especially in procedural or technical documentation. Aggressively stripping them can reduce context and harm retrieval quality.
- D. Incorrect.
This is incorrect. Converting text to uppercase does not meaningfully improve semantic chunking or retrieval relevance. Modern tokenization and embedding approaches do not require case normalization in this way, and uppercasing can remove useful formatting distinctions without addressing the root issue of poor chunk structure.