Collection of prompt templates to us with Amazon Bedrock.

If you are using Llama 3.1 or Llama 3.2, see the following example prompt template: { "anthropic_version": "bedrock-2023-05-31", "system": " $instruction$ You are a helpful assistant with tool calling capabilities. Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.

Contains the template for the prompt that's sent to the model for response generation. For more information, see Knowledge base prompt templates .

The template consists of placeholders that are filled in with user input, the agent configuration, and context at runtime to create a prompt for the foundation model to process when the agent reaches that step. For more information about these placeholders, see Use placeholder variables in Amazon Bedrock agent prompt templates.

Users can copy and paste this template, filling in their own text and few-shot examples to complete prompts when using LLMs on Amazon Bedrock.

This repo has a set of prompt engineering examples and prompting tools for working with Amazon Bedrock.

To declare this entity in your CloudFormation template, use the following syntax: { "Type" : "AWS::Bedrock::Prompt", "Properties" : { "CustomerEncryptionKeyArn" : String, "DefaultVariant" : String, "Description" : String, "Name" : String, "Tags" : {Key: Value, ...}, "Variants" : [ PromptVariant, ...

If you're using Anthropic Claude 3 Sonnet, Claude 3 Haiku, or Claude 3 Opus, the knowledge base response generation prompt template must be raw text, but the pre-processing, orchestration, and post-processing prompt templates must match the JSON format outlined in the Anthropic Claude Messages API. For an example, see the following prompt template: { "anthropic_version": "bedrock-2023-05-31", "system": " $instruction$ You have been provided with a set of functions to answer the user's question.

"Let's create our sample evaluation prompt by leveraging on Prompt Management for Amazon Bedrock. Here, you can adjust the sample prompt template and evaluation criteria provided according to your specific needs."

Users can copy and paste this template, filling in their own text and few-shot examples to complete prompts when using LLMs on Amazon Bedrock.

If you include the genAiResource field, you can only test the prompt in the console. To test a prompt with an agent in the API, you must enter the text of the prompt directly into the inputText field of the InvokeAgent request. templateType – Enter TEXT or CHAT.

import boto3 def generate_text(prompt_template, model_id): client = boto3.client('bedrock') response = client.invoke_model( ModelId=model_id, Body=prompt_template ) return response['body'].read().decode('utf-8') # Prompt template prompt_template = """ Translate the following text from English to Spanish: "Hello, how are you today?" """ model_id = "amazon.titan-fx-large" # Replace with your desired model ID response = generate_text(prompt_template, model_id) print(response) ... Import the Boto3 Library This library allows you to interact with AWS services, including Bedrock.

(Source: Prompt written by AWS) The following content provides guidance on how to create successful prompts. Provide simple, clear, and complete instructions · Place the question or instruction at the end of the prompt for best results · Use separator characters for API calls · Use output indicators · Best practices for good generalization · Optimize prompts for text models on Amazon Bedrock—when the basics aren't good enough ·

If you turn off Override template defaults or change the model, the default Amazon Bedrock template is used and your template will be immediately deleted. To confirm, enter confirm in the text box to confirm the message that appears. To allow the agent to use the template when generating responses, turn on Activate template. If this configuration is turned off, the agent doesn't use the template. To modify the example prompt template, use the Prompt template editor.

Run the following code snippet to load the AWS SDK for Python (Boto3), create a client, and create a prompt that creates a music playlist using two variables (genre and number) by making a CreatePrompt Agents for Amazon Bedrock build-time endpoint:

October 7, 2024 - With our custom prompt template, we get the following response. The ‘promptTemplate‘ option in ‘generationConfiguration‘ allows you to customize the prompt for better control over answer generation. In this post, we introduced two new features in Amazon Bedrock Knowledge Bases: adjusting the maximum number of search results and customizing the default prompt template for the RetrieveAndGenerate API.

For more information, see Configure the prompt templates. Type: String · Length Constraints: Minimum length of 1. Maximum length of 100000. Required: No · foundationModel · The agent's foundation model. Type: String · Length Constraints: Minimum length of 1. Maximum length of 2048. Pattern: (arn:aws(-[^:]{1,12})?:(bedrock|sagemaker):[a-z0-9-]{1,20}:([0-9]{12})?:([a-z-]+/)?)?([a-zA-Z0-9.-]{1,63}){0,2}(([:][a-z0-9-]{1,63}){0,2})?(/[a-z0-9]{1,12})?

Prompt builder – A tool in the Amazon Bedrock console that lets you create, edit, and test prompts and their variants in a visual interface.

You can use placeholder variables in agent prompt templates. The variables will be populated by pre-existing configurations when the prompt template is called. Select a tab to see variables that you can use for each prompt template.