With the release of LLMs like Meta's Llama 4 boasting 10M token context windows, there’s renewed buzz that Retrieval-Augmented Generation (RAG) is becoming obsolete.

But in pharma, where scale, precision, and compliance are non-negotiable, RAG is more relevant than ever.

Consider the data landscape:
🔬 500K+ clinical trial records
📚 200M+ scientific publications
🏥 Terabytes of EHR and real-world evidence

These datasets power critical workflows across Medical Affairs and Commercial team, from answering complex HCP questions and building scientific narratives, to tailoring field strategy and tracking competitor pipelines.

Even the largest context windows can’t load or reason over all this. RAG provides:
✅ Targeted, efficient retrieval
✅ Clear source traceability
✅ Scalable, cost-effective performance

The future is hybrid RAG systems which combine smart retrieval, large context windows, and multi-stage reasoning to deliver insights that are fast and audit-ready.