Question

A developer is building a customer-support application that uses the Amazon Bedrock InvokeModel API directly (not Bedrock Agents). Support requests arrive in six languages and must be classified into one of four ticket categories. The team maintains a prompt template stored in Bedrock Prompt Management with a placeholder for the incoming message. Business analysts frequently add new labeled examples to improve classification accuracy, and they want these updates to take effect without requiring a code deployment. Which approach best satisfies this requirement while keeping the classification consistent?

Accepted Answer

Store the few-shot examples as variables within a versioned prompt in Bedrock Prompt Management, publish a new version when examples change, and reference the prompt by its version identifier at runtime.

Answer

Hardcode the few-shot examples in the application source and redeploy the Lambda function each time analysts submit new examples.

Answer

Fine-tune the foundation model on the labeled examples every time analysts add new ones so the examples are baked into the model weights.

Answer

Place the examples in the model's inferenceConfig stopSequences field so the model uses them during generation without code changes.