You are a cloud architect at a healthcare company that uses OCI Generative AI Service to analyze patient records and generate clinical summaries. The service is deployed in the Frankfurt region with a dedicated AI cluster. Recently, the compliance team flagged that some generated summaries contain hallucinated diagnoses not present in the source records. They demand immediate mitigation. The current setup uses the default model (cohere.command-r-08-2024) with temperature=0.7, top_p=0.9, and max_tokens=2048. The application sends the entire patient record as a single prompt. You have access to OCI Logging, monitoring metrics (latency, request count, token count, safety filter rejections), and the AI service's model fine-tuning capability. You must reduce hallucinations while minimizing latency increase. What is the most effective course of action?
Trap 1: Switch to cohere.command-light model for faster inference and add a…
A lighter model may be faster but likely less accurate; post-processing NER helps but does not prevent hallucinations at generation time.
Trap 2: Increase max_tokens to 4096 and use chunked processing with…
Chunking with overlap may reduce hallucinations by providing more context, but increasing max_tokens increases latency and cost; the improvement might be marginal.
Trap 3: Enable the safety filter with strict content moderation and set up…
Safety filters block harmful content but do not reduce hallucinations about medical facts; auditing only detects issues after the fact.
- A
Switch to cohere.command-light model for faster inference and add a post-processing step using a BERT-based NER model to validate entities.
Why wrong: A lighter model may be faster but likely less accurate; post-processing NER helps but does not prevent hallucinations at generation time.
- B
Increase max_tokens to 4096 and use chunked processing with overlapping context windows to provide more context.
Why wrong: Chunking with overlap may reduce hallucinations by providing more context, but increasing max_tokens increases latency and cost; the improvement might be marginal.
- C
Enable the safety filter with strict content moderation and set up OCI Logging to audit all generations.
Why wrong: Safety filters block harmful content but do not reduce hallucinations about medical facts; auditing only detects issues after the fact.
- D
Reduce temperature to 0.2, top_p to 0.5, and fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8.
Lower temperature/top_p yields more deterministic outputs; fine-tuning on domain-specific data directly reduces hallucinations.