1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
You are a data scientist at a legal firm. The firm uses OCR to digitize court documents and then indexes them in OCI OpenSearch for a RAG application. The application uses OCI Generative AI Service (Cohere Command) to answer questions about case law. Recently, the team noticed that the answers are often factually incorrect or include information not present in the retrieved documents. After reviewing the pipeline, you find that the chunking strategy splits documents into 512-token chunks with 128-token overlap. The embedding model is Cohere Embed v3 (English), and the retrieval returns the top 5 chunks. The LLM has a context window of 4096 tokens. The team suspects that the chunking strategy is causing loss of context. What is the best course of action to improve answer accuracy?
⚠ Common exam trap
Test-takers frequently assume increasing retrieval count (Option D) always improves accuracy, but in RAG systems, more chunks often introduce noise and dilute relevant context, whereas fixing the chunking strategy directly addresses the root cause of context loss.
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
✓
Increase the chunk size to 1024 tokens and overlap to 256 tokens.
Increasing the chunk size to 1024 tokens and overlap to 256 tokens directly addresses the loss of context by ensuring each chunk contains more complete semantic units (e.g., entire paragraphs or legal arguments) while the larger overlap preserves continuity across chunk boundaries. This improves the quality of the embeddings and the relevance of retrieved chunks, leading to more factually accurate answers from the LLM.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the chunk size to 1024 tokens and overlap to 256 tokens.
Why this is correct
Larger chunks with more overlap preserve context better.
- ✗
Reduce the chunk overlap to 64 tokens to avoid redundancy.
Why it's wrong here
Less overlap may lose connections.
- ✗
Switch to a smaller LLM with a larger context window.
Why it's wrong here
Smaller model may be less accurate; context window is already large enough.
- ✗
Increase the number of retrieved chunks from 5 to 10.
Why it's wrong here
May exceed context window and introduce noise.
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