Building LLM Applications with RAG and Vector Search →easyMultiple ChoiceObjective-mapped

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

A retail company uses OCI Generative AI Service to build a RAG chatbot for product recommendations. The chatbot should consider both the user's query and the retrieved product descriptions. Which component of the RAG pipeline is responsible for combining these inputs before sending to the LLM?

⚠ Common exam trap

Oracle often tests the misconception that the embedding model or retriever handles input combination, when in fact those components only deal with vector representation and retrieval, not prompt assembly.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Prompt template

The prompt template is the component in a RAG pipeline that structures the final input to the LLM by combining the user's query with the retrieved product descriptions. It defines the format and instructions (e.g., 'Based on these product descriptions, recommend...') that the LLM uses to generate a coherent response. Without a prompt template, the raw query and documents would be sent without context, leading to poor or irrelevant outputs.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Reranker
Why it's wrong here
Reorders documents; does not combine with query.
✗
Document retriever
Why it's wrong here
Only retrieves documents; does not combine.
✗
Embedding model
Why it's wrong here
Only creates embeddings.
✓
Prompt template
Why this is correct
Merges user query and context into a single prompt.

Go deeper

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Why each option matters

Option-by-option breakdown

Related to this question