RAG vs Fine-Tuning on AWS
RAG adds knowledge; fine-tuning changes behavior. Most teams need RAG first. We help you decide — and build either.
Tempted to fine-tune when RAG (or better prompting) would solve it faster and cheaper? Choosing wrong wastes weeks and budget.
On AWS, RAG (Bedrock Knowledge Bases) injects up-to-date, source-grounded knowledge at inference time, while fine-tuning/customization changes a model’s behavior and style. For most knowledge-grounded use cases, RAG plus good prompting is the right first move; fine-tuning fits when you need consistent format, tone, or task behavior that prompting cannot achieve, and the two can be combined. We help you evaluate the trade-offs (cost, freshness, maintenance, quality), prototype with evaluation, and build RAG, fine-tuning, or both on Bedrock.
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