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Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management.
As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process.
In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents , Amazon Bedrock KnowledgeBases , and Amazon Bedrock Guardrails. Solution overview This section outlines the architecture designed for an email support system using generative AI.
Amazon Bedrock Agents coordinates interactions between foundation models (FMs), knowledgebases, and user conversations. The agents also automatically call APIs to perform actions and access knowledgebases to provide additional information. The documents are chunked into smaller segments for more effective processing.
Companies of all sizes face mounting pressure to operate efficiently as they manage growing volumes of data, systems, and customer interactions. It integrates with existing applications and includes key Amazon Bedrock features like foundation models (FMs), prompts, knowledgebases, agents, flows, evaluation, and guardrails.
They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. The system will take a few minutes to set up your project. On the next screen, leave all settings at their default values.
This transcription then serves as the input for a powerful LLM, which draws upon its vast knowledgebase to provide personalized, context-aware responses tailored to your specific situation. This solution can transform the patient education experience, empowering individuals to make informed decisions about their healthcare journey.
One way to enable more contextual conversations is by linking the chatbot to internal knowledgebases and information systems. Integrating proprietary enterprise data from internal knowledgebases enables chatbots to contextualize their responses to each user’s individual needs and interests.
You can now use Agents for Amazon Bedrock and KnowledgeBases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. System integration – Agents make API calls to integrated company systems to run specific actions.
Infosys Event AI is designed to make knowledge universally accessible, making sure that valuable insights are not lost and can be efficiently utilized by individuals and organizations across diverse industries both during the event and after the event has concluded. MediaConnect securely transmits the stream to MediaLive for processing.
Whether youre an experienced AWS developer or just getting started with cloud development, youll discover how to use AI-powered coding assistants to tackle common challenges such as complex service configurations, infrastructure as code (IaC) implementation, and knowledgebase integration.
During the solution design process, Verisk also considered using Amazon Bedrock KnowledgeBases because its purpose built for creating and storing embeddings within Amazon OpenSearch Serverless. Verisk also has a legal review for IP protection and compliance within their contracts.
Ground truth data in AI refers to data that is known to be factual, representing the expected use case outcome for the system being modeled. By providing an expected outcome to measure against, ground truth data unlocks the ability to deterministically evaluate system quality.
AI agents extend large language models (LLMs) by interacting with external systems, executing complex workflows, and maintaining contextual awareness across operations. Whether youre connecting to external systems or internal data stores or tools, you can now use MCP to interface with all of them in the same way.
Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources.
Error retrieval and context gathering The Amazon Bedrock agent forwards these details to an action group that invokes the first AWS Lambda function (see the following Lambda function code ). This contextual information is then sent back to the first Lambda function. Provide the troubleshooting steps to the user.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. Depending on the use case and data isolation requirements, tenants can have a pooled knowledgebase or a siloed one and implement item-level isolation or resource level isolation for the data respectively.
This standardization is made possible by using advanced prompts in conjunction with KnowledgeBases for Amazon Bedrock , which stores information on organization-specific Terraform modules. In parallel, the AVM layer invokes a Lambda function to generate Terraform code. For creating lambda function, please follow instructions.
With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. An AI assistant is an intelligent system that understands natural language queries and interacts with various tools, data sources, and APIs to perform tasks or retrieve information on behalf of the user.
In the response, you can review the flow traces, which provide detailed visibility into the execution process. These traces help you monitor and debug response times for each step, track the processing of customer inputs, verify if guardrails are properly applied, and identify any bottlenecks in the system.
The interaction shows how AI-powered assistants recognize and plan based on user’s prompts, come up with steps to retrieve context from data stores, and pass through various tools and LLM to arrive at a response. Both the action groups and knowledgebase are optional and not required for the agent itself.
By using Amazon Bedrock Agents , action groups , and Amazon Bedrock KnowledgeBases , we demonstrate how to build a migration assistant application that rapidly generates migration plans, R-dispositions, and cost estimates for applications migrating to AWS. Choose Create knowledgebase and enter a name and optional description.
In this post, we introduce a solution for integrating a “near-real-time human workflow” where humans are prompted by the generative AI system to take action when a situation or issue arises. This can also be a ruled-based method that can determine where, when and how your expert teams can be part of generative AI – user conversations.
Built using Amazon Bedrock KnowledgeBases , Amazon Lex , and Amazon Connect , with WhatsApp as the channel, our solution provides users with a familiar and convenient interface. The result is improved accuracy in FM responses, with reduced hallucinations due to grounding in verified data.
Furthermore, by integrating a knowledgebase containing organizational data, policies, and domain-specific information, the generative AI models can deliver more contextual, accurate, and relevant insights from the call transcripts. The following screenshots show the UI.
They use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledgebases using Retrieval Augmented Generation (RAG) to provide a final response to the end user. We use Amazon Bedrock Agents with two knowledgebases for this assistant.
Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock KnowledgeBases. The ReAct approach enables agents to generate reasoning traces and actions while seamlessly integrating with company systems through action groups.
This solution is intended to act as a launchpad for developers to create their own personalized conversational agents for various applications, such as virtual workers and customer support systems. Amazon Lex then invokes an AWS Lambda handler for user intent fulfillment. ConversationIndexTable – Tracks the conversation state.
This could be Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda , AWS SDK , Amazon SageMaker notebooks, or your workstation if you are doing a quick proof of concept. For the purpose of this post, this code is running on a t3a.micro EC2 instance with Amazon Linux 2023. CUR data stored in an S3 bucket.
Our internal AI sales assistant, powered by Amazon Q Business , will be available across every modality and seamlessly integrate with systems such as internal knowledgebases, customer relationship management (CRM), and more. From the period of September 2023 to March 2024, sellers leveraging GenAI Account Summaries saw a 4.9%
To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledgebases of documents. In Part 1, we review the RAG design pattern and its limitations on analytical questions.
Amazon Kendra can index content from a wide range of sources, including databases, content management systems, file shares, and web pages. This makes it well-suited for powering the retrieval component of a RAG system, allowing the model to access a broad knowledgebase when generating responses. Ask me a question.”
Before introducing the details of the new capabilities, let’s review how prompts are typically developed, managed, and used in a generative AI application. This involves integrating the prompt into a larger system or workflow. This allows teams to track changes, compare performance across versions, and access proven prompts for reuse.
SageMaker also actively monitors instances that are processing inference requests and intelligently routes requests based on which instances are available—achieving 20% lower inference latency (on average). Conjecture, Salesforce, and Slack are already using SageMaker for hosting models due to these inference optimizations.
You can securely integrate and deploy generative AI capabilities into your applications using services such as AWS Lambda , enabling seamless data management, monitoring, and compliance (for more details, see Monitoring and observability ). This is illustrated in the following diagram. Where to start?
In this collaboration, the Generative AI Innovation Center team created an accurate and cost-efficient generative AIbased solution using batch inference in Amazon Bedrock , helping GoDaddy improve their existing product categorization system. It writes the output to another S3 location. It shuts down the endpoint when processing is complete.
This technology allows for automated responses, with only complex cases requiring manual review by a human, streamlining operations and enhancing overall productivity. The combination of retrieval augmented generation (RAG) and knowledgebases enhances automated response accuracy.
IT operations teams face the challenge of providing smooth functioning of critical systems while managing a high volume of incidents filed by end-users. AIOps helps IT teams manage and monitor large-scale systems by automatically detecting, diagnosing, and resolving incidents in real time. This creates visibility gaps for IT teams.
The enterprise AI landscape is undergoing a seismic shift as agentic systems transition from experimental tools to mission-critical business assets. Although agents is the buzzword of 2025, its important to understand what an AI agent is and where deploying an agentic system could yield benefits. billion in 2024 to $47.1
RAG data store The Retrieval Augmented Generation (RAG) data store delivers up-to-date, precise, and access-controlled knowledge from various data sources such as data warehouses, databases, and other software as a service (SaaS) applications through data connectors.
Using graph data processing and the integration of natural language-based search on embedded graphs, these hybrid systems can unlock powerful insights from complex data structures. The RDF is converted into text and loaded into an S3 bucket, which is accessed by Amazon Bedrock (4) as the source of the knowledgebase.
Amazon Bedrock Agents also provides you with traces , a detailed overview of the steps being orchestrated by the agents, the underlying prompts invoking the FM, the references being returned from the knowledgebases, and code being generated by the agent.
The answer lay in using generative AI through Amazon Bedrock Flows, enabling them to build an automated, intelligent request handling system that would transform their client service operations. Path to the solution When evaluating solutions for email triage automation, several approaches appeared viable, each with its own pros and cons.
The solution extracts valuable insights from diverse data sources, including OEM transactions, vehicle specifications, social media reviews, and OEM QRT reports. The system is equipped to understand and respond to these inquiries effectively. This enhances decision-making and competitiveness in the dynamic automotive industry.
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