Build a chatbot that answers from frequently updated internal policies.
Use current documents rather than relying only on pre-training.
What is the primary purpose of RAG?
RAG retrieves relevant documents and adds them to the model context to ground the response in current information.
Detailed explanation
RAG uses external information to ground a generated answer.
RAG uses external information to ground a generated answer.
RAG retrieves context at inference time rather than retraining on every request.
RAG retrieves context at inference time rather than retraining on every request.
Sensitive data must still be protected by access controls.
Sensitive data must still be protected by access controls.
RAG uses retrieved content as context for generation.
RAG uses retrieved content as context for generation.
Try it yourself
An example you can run in a temporary verification environment.
Amazon Bedrock Knowledge Basesの公式ドキュメントで、検索・取得・生成の流れを確認する。Expected result
RAGが再学習ではなく、検索した情報による回答のグラウンディングを行うことを説明できる。Key points
- Retrieval
- Context
- Grounding
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.