Amazon Bedrock introduces new advanced prompt optimization and migration tool | Amazon Web Services

Amazon Bedrock Advanced Prompt Optimization automatically rewrites prompts to improve performance or support migration between models. It compares original and optimized prompts across up to five Bedrock models using example inputs, reference answers, and configurable evaluation methods. The tool supports text and multimodal inputs, then reports quality scores, estimated costs, and latency.

Prompt Optimization and Model Migration

• Users can optimize prompts for their current model or compare a baseline model with up to four alternatives.
• Bedrock runs prompts against supplied examples, evaluates the responses, and iteratively rewrites the prompt.
• Results include:
  • Original and optimized prompt templates
  • Evaluation scores
  • Cost estimates
  • Latency information
• The process can identify regressions in established use cases and improve weaker tasks.

Multimodal and Dataset Support

• Prompt templates and evaluation examples are supplied in JSONL format.
• Each JSON object must appear on a single line.
• Supported multimodal inputs include:
  • PNG and JPG images
  • PDF documents
• Files can be uploaded directly or imported from Amazon S3.
• Optimization results and evaluation data can also be written to an S3 location.

Evaluation Methods

Users can select one evaluation approach per prompt template:

• Lambda-based evaluation

  • Uses custom Python scoring logic for metrics such as accuracy, F1, execution accuracy, or structured JSON matching.
  • The function compares model outputs with reference responses.

• LLM-as-a-judge

  • Uses a custom rubric with named metrics, structured instructions, and rating scales.
  • Bedrock’s judge model evaluates responses and provides scores with reasoning.
  • Claude Sonnet 4.6 is the default judge, though other supported judge models can be selected.

• Natural-language steering criteria

  • Describes desired qualities such as brand voice, formatting, or safety constraints.
  • A default LLM judge evaluates responses holistically against those criteria.

Getting Started and Availability

• Start from Create prompt optimization in the Amazon Bedrock console or use the CreateAdvancedPromptOptimizationJob API.
• Optimization is available in selected AWS Regions across North America, Europe, Asia-Pacific, Canada, and South America.
• Pricing is based on Bedrock inference tokens consumed during optimization, using standard per-token inference rates.

The tool is most useful when teams need measurable prompt improvements or want to migrate applications to another model. Preparing representative evaluation examples and choosing an appropriate scoring method will be essential for producing reliable optimization results.