Amazon Bedrock RAG vs Fine-Tuning: Which Should You Use?
Compare RAG and fine-tuning in Amazon Bedrock using a policy assistant example, with practical checks for retrieval, evaluation, and data freshness.
Choosing between RAG and fine-tuning in Amazon Bedrock starts with identifying what your application is missing. Does it need access to the right information, or does it need to perform a task more consistently?
Consider a university assistant answering questions about course policies. The withdrawal deadline changes each term. Students also expect every answer to follow a clear structure: deadline, eligibility, and next step. These requirements point to different engineering decisions.
What RAG changes
Retrieval-augmented generation supplies external information when a request arrives. In a typical unstructured-document workflow, Amazon Bedrock Knowledge Bases processes source documents into chunks, creates embeddings, and makes those chunks searchable. Relevant passages are retrieved and included in the context sent to the language model.
For the university assistant, that means retrieving the current policy before answering. The model does not need its parameters changed whenever a deadline changes.
However, the knowledge base must actually contain the updated material. For a synced data source, editing the source document alone is insufficient; the corresponding ingestion or synchronization workflow must complete.
What supervised fine-tuning changes
Supervised fine-tuning uses labeled examples to adjust model parameters for a task. The examples teach associations between inputs and desired outputs.
In this scenario, fine-tuning is a candidate for improving a recurring response pattern after simpler prompting approaches have been evaluated. It should not be treated as a live lookup system for deadlines.
Availability also depends on the selected model and supported AWS Region. Check those requirements before designing around a customization job.
Diagnose the failure before changing the architecture
Suppose the assistant confidently returns last term’s date. Inspect the retrieved passages first. If the current policy never reached the model, changing its response style will not repair that retrieval failure.
Now suppose the retrieved policy is correct, but the answer repeatedly omits the eligibility section. Test a clearer prompt and representative examples. Consider fine-tuning if the remaining task-performance gap is measurable and you have suitable training and evaluation data.
RAG and fine-tuning can also be combined where supported: retrieval supplies the evidence, while a customized model handles the task. Combining them adds components to evaluate, so establish a baseline first.
A small evaluation plan
- Evidence: For each test question, identify the source passage that should support the answer.
- Freshness: Change one policy, synchronize the source, and verify that the assistant uses the new version.
- Missing information: Include questions the documents cannot answer. Check whether the assistant clearly acknowledges the gap.
- Task quality: Score whether the answer includes the required fields without adding unsupported claims.
The RetrieveAndGenerate operation can return answers with citations to source material. Those citations make inspection easier; they still need checking to see whether the cited passage supports the actual claim.
The decision I would make
For this policy assistant, I would begin with retrieval and an evaluation set because the central requirement is access to changing documents. I would revisit fine-tuning if task behavior remains inconsistent after retrieval and prompting are working.
Which failure would you investigate first in your own assistant: missing evidence, stale evidence, or inconsistent output?
References
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article