RAG vs Fine-Tuning on AWS: When to Reach for Which
August 15, 20267 min read
RAG changes what a model knows; fine-tuning changes how it behaves. A practical guide to choosing between them on AWS - with the Amazon Bedrock services for each.
What RAG does
Retrieval-augmented generation retrieves relevant documents at query time and passes them to the model as context, so the answer is grounded in your own data. Nothing about the model changes; you are giving it better inputs.
This is the right tool when the problem is missing knowledge: private documents, frequently changing facts, or information the model was never trained on.
What fine-tuning does
Fine-tuning continues training a foundation model on your examples so it changes its behavior - adopting a consistent format, tone, or a narrow task skill. It changes the model's weights, so it costs more and has to be repeated when your needs change.
Reach for it when the problem is behavior, not knowledge: the model knows the facts but will not respond the way you need.
How to choose
Ask what is actually failing. If the model gives confidently wrong answers about facts it should not know, that is a knowledge gap - use RAG. If it knows the material but answers in the wrong style or structure, that is a behavior gap - consider fine-tuning. Many production systems use RAG first and add fine-tuning only if a behavior gap remains.
- Missing or changing facts -> RAG
- Wrong tone, format, or task behavior -> fine-tuning
- Both -> RAG for the facts, fine-tuning for the behavior
The AWS services for each
On Amazon Bedrock, Knowledge Bases provide managed RAG over your documents, while Bedrock's fine-tuning and continued pre-training customize a foundation model on your data. Start with a Knowledge Base; reach for customization when prompting plus retrieval is not enough.
A common exam pattern
A chatbot gives confident, wrong answers about a policy that is not in its training data. The fix is retrieval, not a bigger model or a lower temperature: ground it with a Knowledge Base. Recognizing knowledge gaps versus behavior gaps is exactly what the AIP-C01 exam tests.
Frequently asked questions
- RAG or fine-tuning - which should I use on AWS?
- Use RAG when the model needs facts it was not trained on; fine-tune when you need to change how it behaves. Most systems start with RAG.
- What is RAG on AWS?
- Retrieval-augmented generation. On Amazon Bedrock, Knowledge Bases provide managed RAG over your own documents.
- Is fine-tuning expensive?
- More than RAG - it retrains the model and must be repeated when your needs change, so reach for it only when prompting plus retrieval is not enough.