AI

Incorporating generative artificial intelligence (AI) into your development lifecycle can offer several benefits. For example, using an AI-based coding companion such as Amazon Q Developer can boost development productivity by up to 30 percent. Additionally, reducing the developer context switching that stems from frequent interactions with many different development tools can also increase developer productivity. […]

Organizations spend a lot of resources, effort, and money on running their customer care operations to answer customer questions and provide solutions. Your customers may ask questions through various channels, such as email, chat, or phone, and deploying a workforce to answer those queries can be resource intensive, time-consuming, and unproductive if the answers to

Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and generative language models. RAG models retrieve relevant information from a large corpus of text and then use a generative language model to synthesize an answer based on the retrieved information. The complexity of developing and

ByteDance’s Gauth app scans math homework and provides thorough, often correct, answers using AI. Millions have already downloaded it for free.

When we began studying jailbreak evaluations, we found a fascinating paper claiming that you could jailbreak frontier LLMs simply by translating forbidden prompts into obscure languages. Excited by this result, we attempted to reproduce it and found something unexpected.

Amazon Q Business is a fully managed service that lets you build interactive chat applications using your enterprise data. These applications can generate answers based on your data or a large language model (LLM) knowledge. Your data is not used for training purposes, and the answers provided by Amazon Q Business are based solely on

With the advent of generative AI solutions, a paradigm shift is underway across industries, driven by organizations embracing foundation models to unlock unprecedented opportunities. Amazon Bedrock has emerged as the preferred choice for numerous customers seeking to innovate and launch generative AI applications, leading to an exponential surge in demand for model inference capabilities. Bedrock

Several factors can make remediating security findings challenging. First, the sheer volume and complexity of findings can overwhelm security teams, leading to delays in addressing critical issues. Findings often require a deep understanding of AWS services and configurations and require many cycles for validation, making it more difficult for less experienced teams to remediate issues