Continued Pre-training
Further training an FM on large amounts of unlabeled domain data to build domain knowledge.
Continued pre-training trains a foundation model further on large volumes of unlabeled, domain-specific text — such as medical literature or financial filings — so it internalizes specialized vocabulary and context. Amazon Bedrock offers it as a managed customization job that updates model weights without labeled data. The key exam distinction is the data requirement: continued pre-training uses raw, unlabeled corpora to extend domain knowledge, while fine-tuning needs labeled prompt-and-completion pairs to shape task behavior. Both modify the model’s weights, which separates them from retrieval-augmented generation, which leaves the weights untouched and instead injects external context at inference time.
PlayPrepHQ study notes are written and reviewed against primary exam sources. How we create & review content →