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Automate building guardrails for Amazon Bedrock using test-driven development

AWS Machine Learning

Prerequisites Before you start, make sure you have the following prerequisites in place: Create an AWS account , or sign in to your existing account. For example, a banking assistant application can be designed to deny topics related to illegal investment advice. Have access to the large language model (LLM) that will be used.

APIs 120
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Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock

AWS Machine Learning

The new ApplyGuardrail API enables you to assess any text using your preconfigured guardrails in Amazon Bedrock, without invoking the FMs. In this post, we demonstrate how to use the ApplyGuardrail API with long-context inputs and streaming outputs. For example, you can now use the API with models hosted on Amazon SageMaker.

APIs 132
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How to reframe the banking experience: Defining the new norm for banking contact centers

Talkdesk

The reimagining of business places the customer at its forefront and affects every aspect of the banking industry — from human resources and security to sales and marketing. After COVID-19 hit, many business owners felt underserved by their banks and voiced their displeasure by moving their money elsewhere.

Banking 114
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Getting started with computer use in Amazon Bedrock Agents

AWS Machine Learning

Traditional automation approaches require custom API integrations for each application, creating significant development overhead. Add the Amazon Bedrock Agents supported computer use action groups to your agent using CreateAgentActionGroup API. Prerequisites AWS Command Line Interface (CLI), follow instructions here.

APIs 136
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Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

Its software-as-a-service (SaaS) solution empowers leading banks and lenders with deep customer insights and AI-driven decision-making capabilities. They provide access to external data and APIs or enable specific actions and computation. This post is co-written with Gordon Campbell, Charles Guan, and Hendra Suryanto from RDC.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Similar to how a customer service team maintains a bank of carefully crafted answers to frequently asked questions (FAQs), our solution first checks if a users question matches curated and verified responses before letting the LLM generate a new answer. User submits a question When is re:Invent happening this year?,

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Automate user on-boarding for financial services with a digital assistant powered by Amazon Bedrock

AWS Machine Learning

Onboarding new customers in the banking industry is a crucial step in the customer journey, involving a series of activities designed to fulfill know your customer (KYC) requirements, conduct necessary verifications, and introduce them to the bank’s products or services. Using Anthropic’s Claude 3.5

Banking 121