Category: Generative AI

In this post, I show you how to build a serverless workflow orchestration engine that uses Amazon DynamoDB and AWS Lambda. The complete implementation is available in a GitHub repository, which includes two fully functional examples that you can deploy and run immediately to see the orchestration engine in action.

In April 2025, we introduced the Amazon DynamoDB data modeling tool for the Model Context Protocol (MCP) server. The tool guides you through a conversation, collects your requirements, and produces a data model that includes tables, indexes, and cost considerations. In this post, we show you how we built this automated evaluation framework and how it helped us deliver reliable DynamoDB data modeling guidance at scale.

Amazon DynamoDB, Amazon Bedrock, and LangChain can provide a powerful combination for building robust, context-aware chatbots. In this post, we explore how to use LangChain with DynamoDB to manage conversation history and integrate it with Amazon Bedrock to deliver intelligent, contextually aware responses. We break down the concepts behind the DynamoDB chat connector in LangChain, discuss the advantages of this approach, and guide you through the essential steps to implement it in your own chatbot.

In this post, we explore how to use Amazon Aurora PostgreSQL and Amazon Bedrock to build Federal Risk and Authorization Management Program (FedRAMP) compliant generative artificial intelligence (AI) applications using Retrieval Augmented Generation (RAG).

LeadSquared is a new-age software as a service (SaaS) customer relationship management (CRM) platform that provides end-to-end sales, marketing, and onboarding solutions. Tailored for sectors like BFSI (banking, financial services, and insurance), healthcare, education, real estate, and more, LeadSquared provides a personalized approach for businesses of every scale. LeadSquared Service CRM goes beyond basic ticketing, […]

In recent times, there has been a growing interest in using foundation models (FMs) to build generative AI applications. These models are trained on vast amounts of data and are capable of performing tasks that were previously thought to be the exclusive domain of humans, such as creating art and music. However, when it comes […]

Generative AI has increased the possibilities for businesses to build applications that require searching and comparison of unstructured data types such as text, images, and video. Embeddings, or vectors, capture the meaning and context of this unstructured data in a machine-readable form, which is the basis for how similarity comparisons can be made directly in […]

To be successful with a graph database—such as Amazon Neptune, a managed graph database service—you need a graph data model that captures the data you need and can answer your questions efficiently. Building that model is an iterative process. The earliest stage of the process, in which you are merely getting initial elements on paper […]

Generative AI is a form of artificial intelligence (AI) that is designed to generate content, including text, images, video, and music. In today’s business landscape, harnessing the potential of generative AI has become essential to remain competitive. Foundation models are a form of generative AI. They generate output from one or more inputs (prompts) in […]

In today’s digital age, ecommerce has become an integral part of our lives, offering convenience and endless product options at our fingertips. To enhance online shopping experience, retailers use personalized product recommendations as a key strategy to engage customers and boost sales. Among the cutting-edge technologies fueling this revolution is the vector database, a powerful […]