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/

Cold starts occur when instances scale down to zero and must restart to handle new requests.
Setting a minimum instance count ensures that at least one instance is always running, reducing latency spikes. Increasing timeouts does not address the root cause of cold start delays.

To ensure that older Azure Machine Learning model versions are no longer deployable but remain available for reference or historical tracking, you should archive those specific versions.
How to Archive Model Versions
Archiving a model version effectively hides it from standard list queries and management interfaces, preventing accidental deployment while maintaining its metadata and files in the workspace.
Behavior of Archived Models:
Hidden by Default: They will no longer appear in the Azure Machine Learning Studio model list unless you explicitly filter for archived assets.
Referenceable: You can still reference and use an archived model version in existing workflows if you have its specific version number.
Restoreable: If you need to redeploy the model in the future, you can use the restore command to make it active again.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models