3) Out-of-date knowledge: Build a living pipeline that updates itself

The problem: Base LLM knowledge is always months behind—dangerous when dosing or resistance guidance changes.

The fix: Automate a data refresh → index → retrieve loop tied to authoritative endpoints.

A practical pipeline:

1. Scheduled ingestion (e.g., cron/Airflow/GitHub Actions).
   • Package-inserts: daily/weekly/monthly dumps of all labels.
   • Regulatory APIs/data: download zipped datasets for updates.
   • Clinical guidance: use content APIs to keep guidance in sync.
   • Specialist society updates: monitor major guideline updates (e.g., antimicrobial-resistance treatment).
2. Normalize & version. Parse to a common schema (drug → ingredient(s) → dose forms → indications → contraindications) and stamp the source date so every recommendation can be reproduced “as of” a specific day. (Provenance is your audit trail.)
3. Chunk, embed, and index. Use a vector store such as FAISS, Milvus, or pgvector; choose ANN indexes (HNSW/IVF) appropriate for corpus size and latency.
4. Retrieval policy. At runtime, retrieve only documents newer than the last guideline update and filter by jurisdiction (e.g., US labeling for US patients). Include the “as-of” date in the report footer.
5. Human-in-the-loop & safety checks. Route novel or conflicting cases to stewardship pharmacists/ID specialists; log every source snippet and model hash for QA.

Final thoughts

For infectious-disease lab reports, accuracy, consistency, and currency are non-negotiable. Grounding with RAG, taming generation with deterministic settings and classification, and operating a living data pipeline transform LLMs from risky free-form generators into traceable clinical summarizers that cite and conform to regulatory and society sources. Always treat the model as a documentation assistant, not an autonomous prescriber, and keep a clinician in the loop for exceptions and governance.

References

1. „Reducing hallucinations in structured outputs via Retrieval-Augmented Generation”, ArXiv 2024.
2. „Understanding retrieval-augmented generation (RAG): a response to hallucinations”, Ontoforce Blog.
3. „How to Prevent LLM Hallucinations: 5 Proven Strategies”, Voiceflow Blog.
4. „Outpatient Clinical Care for Adults | Antibiotic Prescribing and Use”, CDC.
5. „Infectious Diseases Society of America 2024 Guidance on the Treatment of Antimicrobial-Resistant Gram-Negative Infections”, IDSA.
6. „Core Elements of Hospital Antibiotic Stewardship Programs”, CDC.

Disclaimer: This post describes engineering patterns for report generation and is not medical advice. Clinical decisions must be made by licensed professionals using full patient context.