Both options B and D describe valid and effective ways to implement a RAG pipeline in Snowflake. * *Option B is correct." Cortex Search is specifically designed to act as a RAG engine for LLM chatbots, enabling high-quality fuzzy search over text data. Creating a 'CORTEX SEARCH SERVICE on the text column (e.g., 'transcript_text') and enabling 'CHANGE_TRACKING' on the source table are fundamental steps for continuous updates and retrieval. The retrieved results then provide the necessary context for an LLM call using 'SNOWFLAKE.CORTEX.COMPLETE (or 'AI_COMPLETE). "Option D is correct." This option outlines a more manual, but equally effective, approach to RAG. It involves generating vector embeddings for text chunks using functions like (or the newer 'AI_EMBED), storing these embeddings in a 'VECTOR data type column, and then performing a similarity search (e.g., with *VECTOR COSINE_SIMILARITY) to retrieve relevant content based on a user's query. The retrieved text then serves as context for the LLM (SNOWFLAKE.CORTEX.COMPLETE or 'AI_COMPLETE) to generate a grounded response. Option A is incorrect. While 'SUMMARIZE can produce summaries, it doesn't provide a mechanism for retrieving *semantically similar* text chunks from a large corpus based on a user query, which is crucial for RAG. Storing data in a 'VARIANT' column is also generally not optimal for direct vector embedding or search service operations as described in RAG contexts. Option C is incorrect. Document AI ( ' !PREDICT) is primarily for extracting structured information (entities, tables) from documents, not for general semantic search or RAG over free-form text with LLMs. 'EXTRACT ANSWER is used to find answers within a given text, but it's not the primary orchestrator for a dynamic RAG pipeline that first needs to *retrieve* relevant documents. Option E is incorrect. Cortex Analyst is designed for text-to-SQL functionality over *structured data* and its semantic models, not directly for RAG over *unstructured text corpora'. While it can integrate with Cortex Search for improving literal values in SQL queries, its core purpose is not to act as a RAG engine for unstructured document chat.