Category: Technical How-to

In this post, we demonstrate how to use Amazon Bedrock and LangGraph to build a personalized customer support experience for an ecommerce retailer. By integrating the Mistral Large 2 and Pixtral Large models, we guide you through automating key customer support workflows such as ticket categorization, order details extraction, damage assessment, and generating contextual responses.

In this post, we demonstrate how Amazon Bedrock Guardrails helps block harmful and undesirable multimodal content. Using a healthcare insurance call center scenario, we walk through the process of configuring and testing various guardrails.

E.ON’s story highlights how a creative application of Amazon Textract, combined with custom image analysis and pulse counting, can solve a real-world challenge at scale. By diagnosing smart meter errors through brief smartphone videos, E.ON aims to lower costs, improve customer satisfaction, and enhance overall energy service reliability. In this post, we dive into how this solution works and the impact it’s making.

In this post, we explore the implementation details of a web application that uses the browser’s Web Audio API and Amazon Transcribe streaming to enable real-time dual-channel transcription. By using the combination of AudioContext, ChannelMergerNode, and AudioWorklet, we were able to seamlessly process and encode the audio data from two microphones before sending it to Amazon Transcribe for transcription.

In this post, we demonstrate how to use the Open AI Whisper foundation model (FM) Whisper Large V3 Turbo, available in Amazon Bedrock Marketplace, which offers access to over 140 models through a dedicated offering, to produce near real-time transcription. These transcriptions are then processed by Amazon Bedrock for summarization and redaction of sensitive information.

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on Amazon SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use Amazon OpenSearch Serverless with its vector engine for low-latency semantic video search.

This post evaluates the key options for querying data using generative AI, discusses their strengths and limitations, and demonstrates why Text-to-SQL is the best choice for deterministic, schema-specific tasks. We show how to effectively use Text-to-SQL using Amazon Nova, a foundation model (FM) available in Amazon Bedrock, to derive precise and reliable answers from your data.

In this post, we’ll explore how to combine Amazon Bedrock prompt caching with Claude Code—a coding agent released by Anthropic that is now generally available. This powerful combination transforms your development workflow by delivering lightning-fast responses from reducing inference response latency, as well as lowering input token costs.

We’ve witnessed remarkable advances in model capabilities as generative AI companies have invested in developing their offerings. Language models such as Anthropic’s Claude Opus 4 & Sonnet 4 and Amazon Nova on Amazon Bedrock can reason, write, and generate responses with increasing sophistication. But even as these models grow more powerful, they can only work […]

In this post, we explore how to use Graph-based Retrieval-Augmented Generation (GraphRAG) in Amazon Bedrock Knowledge Bases to build intelligent applications. Unlike traditional vector search, which retrieves documents based on similarity scores, knowledge graphs encode relationships between entities, allowing large language models (LLMs) to retrieve information with context-aware reasoning.