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Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

Advanced (300), AI/ML, AIML, Amazon Bedrock, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker Data & AI Governance, Generative AI, Responsible AI /

Generative AI is rapidly reshaping industries worldwide, empowering businesses to deliver exceptional customer experiences, streamline processes, and push innovation at an unprecedented scale. However, amidst the excitement, critical questions around the responsible use and implementation of such powerful technology have started to emerge. Although responsible AI has been a key focus for the industry over […]

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Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge

Advanced (300), Amazon Bedrock Agents, Technical How-to, Thought Leadership /

AI agents are quickly becoming an integral part of customer workflows across industries by automating complex tasks, enhancing decision-making, and streamlining operations. However, the adoption of AI agents in production systems requires scalable evaluation pipelines. Robust agent evaluation enables you to gauge how well an agent is performing certain actions and gain key insights into

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Build an automated generative AI solution evaluation pipeline with Amazon Nova

Advanced (300), Amazon Bedrock, Amazon Nova, Customer Solutions /

Large language models (LLMs) have become integral to numerous applications across industries, ranging from enhanced customer interactions to automated business processes. Deploying these models in real-world scenarios presents significant challenges, particularly in ensuring accuracy, fairness, relevance, and mitigating hallucinations. Thorough evaluation of the performance and outputs of these models is therefore critical to maintaining trust

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Amazon Bedrock Prompt Optimization Drives LLM Applications Innovation for Yuewen Group

Advanced (300), Amazon Bedrock, Customer Solutions, Generative AI /

Yuewen Group is a global leader in online literature and IP operations. Through its overseas platform WebNovel, it has attracted about 260 million users in over 200 countries and regions, promoting Chinese web literature globally. The company also adapts quality web novels into films, animations for international markets, expanding the global influence of Chinese culture.

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Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation model

Advanced (300), Amazon Bedrock, Amazon Cognito, Amazon Machine Learning, Amazon Nova, Artificial Intelligence, AWS Amplify, AWS Cost Explorer, AWS Lambda, AWS Trusted Advisor /

AI agents are revolutionizing how businesses enhance their operational capabilities and enterprise applications. By enabling natural language interactions, these agents provide customers with a streamlined, personalized experience. Amazon Bedrock Agents uses the capabilities of foundation models (FMs), combining them with APIs and data to process user requests, gather information, and execute specific tasks effectively. The

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Build multi-agent systems with LangGraph and Amazon Bedrock

Advanced (300), AI/ML, Amazon Bedrock, Artificial Intelligence, Generative AI, Technical How-to /

Large language models (LLMs) have raised the bar for human-computer interaction where the expectation from users is that they can communicate with their applications through natural language. Beyond simple language understanding, real-world applications require managing complex workflows, connecting to external data, and coordinating multiple AI capabilities. Imagine scheduling a doctor’s appointment where an AI agent

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Model customization, RAG, or both: A case study with Amazon Nova

Advanced (300), Amazon Bedrock, Amazon Machine Learning, Amazon Nova, Amazon SageMaker, Generative AI, Technical How-to /

As businesses and developers increasingly seek to optimize their language models for specific tasks, the decision between model customization and Retrieval Augmented Generation (RAG) becomes critical. In this post, we seek to address this growing need by offering clear, actionable guidelines and best practices on when to use each approach, helping you make informed decisions

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Create a virtual stock technical analyst using Amazon Bedrock Agents

Advanced (300), Amazon Bedrock, Amazon Bedrock Agents, Financial Services, Generative AI /

Stock technical analysis questions can be as unique as the individual stock analyst themselves. Queries often have multiple technical indicators like Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), and others. Answering these varied questions would mean writing complex business logic to unpack the query into parts and fetching the necessary

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Transform customer engagement with no-code LLM fine-tuning using Amazon SageMaker Canvas and SageMaker JumpStart

Advanced (300), Amazon SageMaker, Amazon SageMaker Canvas, Amazon SageMaker JumpStart, Artificial Intelligence /

Fine-tuning large language models (LLMs) creates tailored customer experiences that align with a brand’s unique voice. Amazon SageMaker Canvas and Amazon SageMaker JumpStart democratize this process, offering no-code solutions and pre-trained models that enable businesses to fine-tune LLMs without deep technical expertise, helping organizations move faster with fewer technical resources. SageMaker Canvas provides an intuitive

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Improve accuracy of Amazon Rekognition Face Search with user vectors

Advanced (300), Amazon Machine Learning, Amazon Rekognition /

In various industries, such as financial services, telecommunications, and healthcare, customers use a digital identity process, which usually involves several steps to verify end-users during online onboarding or step-up authentication. An example of one step that can be used is face search, which can help determine whether a new end-user’s face matches those associated with

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