Technical How-to

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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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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

Amazon Bedrock, Artificial Intelligence, Generative AI, Technical How-to /

This blog post is co-written with Renuka Kumar and Thomas Matthew from Cisco. Enterprise data by its very nature spans diverse data domains, such as security, finance, product, and HR. Data across these domains is often maintained across disparate data environments (such as Amazon Aurora, Oracle, and Teradata), with each managing hundreds or perhaps thousands

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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

AI/ML, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence, Customer Solutions, Generative AI, Natural Language Processing, Technical How-to /

Archival data in research institutions and national laboratories represents a vast repository of historical knowledge, yet much of it remains inaccessible due to factors like limited metadata and inconsistent labeling. Traditional keyword-based search mechanisms are often insufficient for locating relevant documents efficiently, requiring extensive manual review to extract meaningful insights. To address these challenges, a

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Host concurrent LLMs with LoRAX

Amazon EC2, Artificial Intelligence, Generative AI, Technical How-to /

Businesses are increasingly seeking domain-adapted and specialized foundation models (FMs) to meet specific needs in areas such as document summarization, industry-specific adaptations, and technical code generation and advisory. The increased usage of generative AI models has offered tailored experiences with minimal technical expertise, and organizations are increasingly using these powerful models to drive innovation and

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Optimizing Mixtral 8x7B on Amazon SageMaker with AWS Inferentia2

AI/ML, Amazon EC2, Amazon SageMaker, AWS Inferentia, AWS Neuron, Generative AI, Technical How-to /

Organizations are constantly seeking ways to harness the power of advanced large language models (LLMs) to enable a wide range of applications such as text generation, summarizationquestion answering, and many others. As these models grow more powerful and capable, deploying them in production environments while optimizing performance and cost-efficiency becomes more challenging. Amazon Web Services

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Elevate business productivity with Amazon Q and Amazon Connect

Amazon Connect, Amazon Q, Amazon Q Business, Best Practices, Generative AI, Technical How-to /

Modern banking faces dual challenges: delivering rapid loan processing while maintaining robust security against sophisticated fraud. Amazon Q Business provides AI-driven analysis of regulatory requirements and lending patterns. Additionally, you can now report fraud from the same interface with a custom plugin capability that can integrate with Amazon Connect. This fusion of technology transforms traditional

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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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Dynamic text-to-SQL for enterprise workloads with Amazon Bedrock Agents

Amazon Bedrock, Amazon Bedrock Agents, Best Practices, Generative AI, Technical How-to, Thought Leadership /

Generative AI enables us to accomplish more in less time. Text-to-SQL empowers people to explore data and draw insights using natural language, without requiring specialized database knowledge. Amazon Web Services (AWS) has helped many customers connect this text-to-SQL capability with their own data, which means more employees can generate insights. In this process, we discovered

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Building an AIOps chatbot with Amazon Q Business custom plugins

AI/ML, Amazon Q Business, Technical How-to /

Many organizations rely on multiple third-party applications and services for different aspects of their operations, such as scheduling, HR management, financial data, customer relationship management (CRM) systems, and more. However, these systems often exist in silos, requiring users to manually navigate different interfaces, switch between environments, and perform repetitive tasks, which can be time-consuming and

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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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