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Create and manage optimization jobs - Amazon Bedrock
Create a job (console)Create a job (Python SDK)Create a job (AWS CLI)Check job statusList jobsStop a jobBatch delete jobs

Create and manage optimization jobs

Create a job (console)

  1. Give your advanced prompt optimization job a name.

  2. Select your target models (up to 5 per job). If you are using this tool to help you migrate to a new model, best practice is to select your current model as a baseline and up to 4 other models you are interested in migrating to. If you are not changing models, just select your current model.

  3. Upload your JSONL input file from S3 or from your computer. Make sure you follow the Prepare your input dataset.

  4. For each prompt template, select your evaluation method. You can choose between steering criteria, LLM-as-a-judge, Lambda function, or leave blank for the system default.

  5. Select your output location in S3 for your raw results to be saved after the job.

  6. (Optional) Add a job description or a custom KMS key for encryption.

  7. Create the optimization.

Note

If you move the results files away from the output S3 location after the job is complete, the console results page will not render.

Create a job (Python SDK)

Replace the region, S3 bucket location, model ID, and other fields as necessary for your use case.

import boto3
import json

client = boto3.client('bedrock', region_name='us-west-2')

response = client.create_advanced_prompt_optimization_job(
    jobName='my-optimization-job',
    modelConfigurations=[
        {'modelId': 'us.anthropic.claude-sonnet-4-5-20250929-v1:0'}
    ],
    inputConfig={
        's3Uri': 's3://my-bucket/input/dataset.jsonl'
    },
    outputConfig={
        's3Uri': 's3://my-bucket/output/'
    }
)

job_arn = response['jobArn']
print(f'Job created: {job_arn}')

Create a job (AWS CLI)

Replace the region, S3 bucket location, model ID, and other fields as necessary for your use case.

JOB_ARN=$(aws bedrock create-advanced-prompt-optimization-job \
    --job-name "my-optimization-job" \
    --model-configurations '[{"modelId": "us.anthropic.claude-sonnet-4-5-20250929-v1:0"}]' \
    --input-config '{"s3Uri": "s3://my-bucket/input/dataset.jsonl"}' \
    --output-config '{"s3Uri": "s3://my-bucket/output/"}' \
    --region us-west-2 \
    --query 'jobArn' --output text)

Check job status

response = client.get_advanced_prompt_optimization_job(
    jobIdentifier=job_arn
)

print(f"Status: {response['jobStatus']}")
if response.get('failureMessage'):
    print(f"Failure: {response['failureMessage']}")
aws bedrock get-advanced-prompt-optimization-job \
    --job-identifier "$JOB_ARN" \
    --region us-west-2

List jobs

response = client.list_advanced_prompt_optimization_jobs(
    maxResults=10
)

for job in response['advancedPromptOptimizationJobSummaries']:
    print(f"{job['jobName']} | {job['jobStatus']} | {job['jobArn']}")
aws bedrock list-advanced-prompt-optimization-jobs \
    --max-results 10 \
    --region us-west-2

Stop a job

client.stop_advanced_prompt_optimization_job(
    jobIdentifier=job_arn
)
print('Job stop requested')
aws bedrock stop-advanced-prompt-optimization-job \
    --job-identifier "$JOB_ARN" \
    --region us-west-2

Batch delete jobs

response = client.batch_delete_advanced_prompt_optimization_jobs(
    jobIdentifiers=[job_arn]
)

for result in response['results']:
    print(f"{result['jobIdentifier']}: {result['status']}")
aws bedrock batch-delete-advanced-prompt-optimization-jobs \
    --job-identifiers "$JOB_ARN" \
    --region us-west-2