# AWS Bedrock

By integrating your AWS Bedrock, you can unlock a range of powerful features in the platform, including:

1. **Generating responses to prompts in a dataset** – Automate the creation of text outputs (such as classification, summarization, or creative writing) across large collections of data.
2. **Rapid experimentation in the Playground** – Quickly prototype, iterate, and test prompts in a user-friendly environment without the overhead of a production workflow.
3. **Using Large Language Models (LLMs) as a “judge” for metrics** – Leverage LLMs to evaluate and score the quality of generated outputs or compare different model outputs.
4. **Generating responses in production applications with guardrails** – Integrate LLM output directly into your product or system, while employing guardrails to ensure appropriate, safe, and reliable responses.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.raga.ai/ragaai-catalyst/concepts/supported-llms/aws-bedrock.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.