interface PromptInferenceConfigurationProps 🔹

Language	Type name
.NET	Amazon.CDK.AWS.Bedrock.Alpha.PromptInferenceConfigurationProps
Go	github.com/aws/aws-cdk-go/awsbedrockalpha/v2#PromptInferenceConfigurationProps
Java	software.amazon.awscdk.services.bedrock.alpha.PromptInferenceConfigurationProps
Python	aws_cdk.aws_bedrock_alpha.PromptInferenceConfigurationProps
TypeScript (source)	@aws-cdk/aws-bedrock-alpha » PromptInferenceConfigurationProps

Properties for creating a prompt inference configuration.

Example

const cmk = new kms.Key(this, 'cmk', {});
const claudeModel = bedrock.BedrockFoundationModel.ANTHROPIC_CLAUDE_SONNET_V1_0;

const variant1 = bedrock.PromptVariant.text({
  variantName: 'variant1',
  model: claudeModel,
  promptVariables: ['topic'],
  promptText: 'This is my first text prompt. Please summarize our conversation on: {{topic}}.',
  inferenceConfiguration: bedrock.PromptInferenceConfiguration.text({
    temperature: 1.0,
    topP: 0.999,
    maxTokens: 2000,
  }),
});

const prompt1 = new bedrock.Prompt(this, 'prompt1', {
  promptName: 'prompt1',
  description: 'my first prompt',
  defaultVariant: variant1,
  variants: [variant1],
  kmsKey: cmk,
});

Properties

Name	Type	Description
maxTokens?🔹	number	The maximum number of tokens to return in the response.
stopSequences?🔹	string[]	A list of strings that define sequences after which the model will stop generating.
temperature?🔹	number	Controls the randomness of the response.
topP?🔹	number	The percentage of most-likely candidates that the model considers for the next token.

---

maxTokens?🔹

Type: number (optional, default: No limit specified)

The maximum number of tokens to return in the response.

---

stopSequences?🔹

Type: string[] (optional, default: No stop sequences)

A list of strings that define sequences after which the model will stop generating.

---

temperature?🔹

Type: number (optional, default: Model default temperature)

Controls the randomness of the response.

Higher values make output more random, lower values more deterministic. Valid range is 0.0 to 1.0.

---

topP?🔹

Type: number (optional, default: Model default topP)

The percentage of most-likely candidates that the model considers for the next token.

Valid range is 0.0 to 1.0.