SnowPro Specialty: Gen AI Question 50
Single answerVector-embeddingA retail company is building a retrieval-augmented generation (RAG) assistant in Snowflake to answer questions about product manuals and return policies. They have already chunked the source documents and generated vector embeddings for each chunk using the same embedding model. Now they need to implement semantic retrieval so that a user question returns the most relevant chunks before sending them to a large language model. Which approach should they use?
- A
Generate an embedding for the user question with the same embedding model, then compare that query vector to stored chunk vectors using a vector similarity function and return the top matches.
- B
Store the user question as plain text and use only a SQL equality join against the chunk text to find the most relevant documents.
- C
Convert each stored embedding into a larger dimensional vector so that Snowflake can rank results more accurately without recalculating the query embedding.
- D
Use a text generation model to summarize each chunk at query time, then rank chunks only by summary length because shorter summaries are more semantically precise.
Show answer and explanation
Correct answer: A
Explanation
For semantic retrieval in Snowflake-based RAG solutions, best practice is to embed both the corpus chunks and the incoming query using the same embedding model, then compare vectors using an appropriate similarity function to retrieve the nearest neighbors. Using the same model is important because embeddings from different models may not share a compatible vector space, which can degrade or invalidate similarity results. Exact text matching or arbitrary vector manipulation does not solve the semantic retrieval problem. This aligns with Snowflake Cortex embedding and vector search best practices, where embeddings are used to support similarity-based retrieval prior to passing context to an LLM for answer generation.
- A. Correct.
Correct. In a vector search workflow, the user query is embedded using the same embedding model that was used for the document chunks so both are represented in the same vector space. The system then computes similarity between the query embedding and stored embeddings, typically using a similarity or distance metric, and returns the top-ranked chunks for downstream generation. This is the standard semantic retrieval pattern used in RAG systems.
- B. Incorrect.
Incorrect. Equality joins on plain text do not perform semantic matching. They only find exact or directly matching text values and would miss relevant chunks that use different wording but have similar meaning. This reflects a common misconception that SQL text matching can substitute for embedding-based retrieval in semantic search.
- C. Incorrect.
Incorrect. Arbitrarily increasing vector dimensionality does not improve retrieval quality and is not how embeddings work. Embedding dimensions are defined by the model that produced them. Changing dimensions without regenerating embeddings from a valid model would make vectors incompatible and unusable for similarity comparison.
- D. Incorrect.
Incorrect. Summarizing chunks at query time adds latency and does not provide a reliable semantic ranking signal. Ranking by summary length is unrelated to semantic relevance. This option mixes generation tasks with retrieval logic and confuses summarization with vector-based similarity search.