AWS solutions documentation change
Summary
Formatting changes including hyphen corrections, image caption restructuring, VPC configuration typo fixes, model selection documentation updates, inference profile table formatting, and IAM permissions list restructuring
Security assessment
Changes are primarily stylistic improvements, typo corrections, and documentation restructuring. While IAM permissions are mentioned, this is existing security documentation being reformatted rather than new security content. No concrete evidence of addressing vulnerabilities or security incidents.
Diff
diff --git a/solutions/latest/generative-ai-application-builder-on-aws/step-3-deploy-a-use-case-using-deployment-dashboard-wizard.md index ec611ce15..e8b1dacb1 100644 --- a/solutions/latest/generative-ai-application-builder-on-aws/step-3-deploy-a-use-case-using-deployment-dashboard-wizard.md +++ b/solutions/latest/generative-ai-application-builder-on-aws/step-3-deploy-a-use-case-using-deployment-dashboard-wizard.md @@ -11 +11 @@ In the Deployment dashboard wizard, you must choose between the following: - * Text use case – Deploys a chat application, with optional RAG capabilities + * Text use case \- Deploys a chat application, with optional RAG capabilities @@ -13 +13 @@ In the Deployment dashboard wizard, you must choose between the following: - * Agent use case – Uses Amazon Bedrock Agents to complete tasks or automate repeated workflows + * Agent use case \- Uses Amazon Bedrock Agents to complete tasks or automate repeated workflows @@ -18 +18,3 @@ In the Deployment dashboard wizard, you must choose between the following: - +**Shows two options: Create Text use case or Create Agent use case.** + + @@ -68 +70 @@ Additionally, if you need to add more users to a use case, refer to the [Managin -This wizard step allows you to deploy the use case with a pre-existing or new [Amazon Virtual Private Cloud](https://aws.amazon.com/vpc/) (Amazon VPC). If selecting pre-existing VPC, you are required to provide a VPC ID, up to 16 subnet Ids and up to 5 security group IDs to use with this VPC. If you're not using a pre-existing VPC, these settings will be configured for you. +This wizard step allows you to deploy the use case with a pre-existing or new [Amazon Virtual Private Cloud](https://aws.amazon.com/vpc/) (Amazon VPC). If selecting pre-existing VPC, you are required to provide a VPC ID, up to 16 subnet Ids and up to 5 security group IDs to use with this VPC. If you’re not using a pre-existing VPC, these settings will be configured for you. @@ -72 +74 @@ This wizard step allows you to deploy the use case with a pre-existing or new [A -In the **Select model** step, you can choose your model provider, such as **Amazon Bedrock** , and select a model from the available models’ names. Alternatively, you can create a SageMaker AI model endpoint in the SageMaker AI console and provide the input schema that the model expects and output JSONPath for the LLM response. You can refer to the [Using Amazon SageMaker as an LLM Provider](./configuring-an-llm.html#using-amazon-sagemaker-as-an-llm-provider) section and [_SageMaker payload examples_](https://github.com/aws-solutions/generative-ai-application-builder-on-aws/tree/main/docs/sagemaker-payload-examples) provided in the solution's GitHub repository. +In the **Select model** step, you can choose your model provider, such as **Amazon Bedrock** , and select a model from the available models names. Alternatively, you can create a SageMaker AI model endpoint in the SageMaker AI console and provide the input schema that the model expects and output JSONPath for the LLM response. You can refer to the [Using Amazon SageMaker as an LLM Provider](./use-the-solution.html#using-amazon-sagemaker-as-an-llm-provider) section and [SageMaker payload examples](https://github.com/aws-solutions/generative-ai-application-builder-on-aws/tree/main/docs/sagemaker-payload-examples) provided in the solution’s GitHub repository. @@ -74 +76 @@ In the **Select model** step, you can choose your model provider, such as **Amaz -Model selection step also allows you to choose your advanced model settings. Refer to [Advanced LLM settings](./advanced-llm-settings.html) for details on configuring Amazon Bedrock Guardrails, provisioned throughput for Amazon Bedrock, and additional model parameters. +Model selection step also allows you to choose your advanced model settings. Refer to [Advanced LLM settings](./advanced-llm-settings.html) for details on configuring Amazon Bedrock Guardrails, provisioned throughput for Amazon Bedrock, and additional model parameters. @@ -82 +84 @@ Inference profiles are named after the model and Regions that they support. You -**Inference profile** | **Inference profile ID** | **Regions included** +Inference profile | Inference profile ID | Regions included @@ -103 +105 @@ Refer to [Improve resilience with cross-region inference](https://docs.aws.amazo -If you're looking to deploy a non-Retrieval Augmented Generation (RAG) use case, you can skip this step. +If you’re looking to deploy a non-Retrieval Augmented Generation (RAG) use case, you can skip this step. @@ -119 +121 @@ Your knowledge base must be in the same account and Region as the deployed Deplo -In this step, you can configure your prompt for use with the LLM. Each prompt requires a minimum of two placeholders – `{input}` and `{history}`. For RAG use cases, the `{context}` placeholder is additionally required. These placeholders instruct the LLM on where to draw user input, conversation history, and information retrieved from the knowledge base from. +In this step, you can configure your prompt for use with the LLM. Each prompt requires a minimum of two placeholders - `{input}` and `{history}`. For RAG use cases, the `{context}` placeholder is additionally required. These placeholders instruct the LLM on where to draw user input, conversation history, and information retrieved from the knowledge base from. @@ -135 +137,5 @@ Before creating an Amazon Bedrock agent, ensure that you have the following: -1\. The AWS account where Generative AI Application Builder on AWS is deployed, with an access to the Amazon Bedrock console. + 1. The AWS account where Generative AI Application Builder on AWS is deployed, with an access to the Amazon Bedrock console. + + 2. Appropriate IAM permissions to create and manage Amazon Bedrock Agents. + + @@ -137 +142,0 @@ Before creating an Amazon Bedrock agent, ensure that you have the following: -2\. Appropriate IAM permissions to create and manage Amazon Bedrock Agents. @@ -160 +165 @@ This step is the same as the Text use case described previously. -This step is the same as the Text use case described previously. . +This step is the same as the Text use case described previously