Exam AI-300 Topic 1 Question 22 Discussion

To best improve retrieval accuracy when documents are irrelevant or incomplete, you should adjust Chunk size and overlap.
Fixes Irrelevant Retrievals: If your chunks are too large, they pack in multiple unrelated topics.
This adds noise, dilutes the semantic vector, and causes the system to pull in massive blocks of text where only a single sentence was actually relevant. Tuning the size ensures chunks stay highly specific.
Fixes Incomplete Retrievals: If your chunks are too small or lack sufficient overlap, vital context gets completely cut off at the boundaries. Adding an optimal overlap (typically 10%-20%) ensures that a sentence split across a boundary preserves its surrounding meaning, keeping the context whole.
Incorrect:
[Not B]
Temperature parameter: This is a generative LLM setting that dictates the creativity or randomness of the output text. It has absolutely zero control over the vector search database or the specific documents picked during the initial retrieval phase.
[Not C]¨
Token limits: This caps how much context you can pass into the prompt window or how much text the model can generate. While a higher limit lets you pass more documents, it does not magically fix the underlying data quality or prevent the retrieval algorithm from pulling in wrong files.
[Not D]
Embedding strategy: While choosing a stronger model or fine-tuning embeddings changes how text is numerically represented, it cannot overcome poorly structured data. Even the best embedding model cannot successfully capture semantic meaning if your data ingestion pipeline is feeding it randomly sliced, incomplete paragraphs Reference:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12649634/