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RAG isn’t disappearing: it’s becoming foundational. In pharma’s data-rich, compliance-driven world, smart retrieval paired with large context models is the only path to scalable, audit-ready AI.
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 teams—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 isn’t RAG vs. long context. It’s hybrid systems—combining smart retrieval, large context windows, and multi-stage reasoning to deliver insights that are fast, factual, and audit-ready.
RAG isn’t disappearing—it’s becoming foundational to enterprise AI in pharma.
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