Developing generative AI agents that can tackle real-world tasks is complex, and building production-grade agentic applications requires integrating agents with additional tools such as user interfaces, evaluation frameworks, and continuous improvement mechanisms. Developers often find themselves grappling with unpredictable...
In the drive to remain competitive, businesses today are turning to AI to help them minimize cost and maximize efficiency. It’s incumbent on them to find the most suitable AI model—the one that will help them achieve more while...
Organizations deploying generative AI applications need robust ways to evaluate their performance and reliability. When we launched LLM-as-a-judge (LLMaJ) and Retrieval Augmented Generation (RAG) evaluation capabilities in public preview at AWS re:Invent 2024, customers used them to assess their...
This post is co-written with Paul Pagnan from Lumi.
Lumi is a leading Australian fintech lender empowering small businesses with fast, flexible, and transparent funding solutions. They use real-time data and machine learning (ML) to offer customized loans that fuel...
Large language models (LLMs) can be used to perform natural language processing (NLP) tasks ranging from simple dialogues and information retrieval tasks, to more complex reasoning tasks such as summarization and decision-making. Prompt engineering and supervised fine-tuning, which use...
This post is co-written with Emrah Kaya and Xinyi Zhou from Omron Europe.
Data is one of the most critical assets of many organizations. They’re constantly seeking ways to use their vast amounts of information to gain competitive advantages.
OMRON Corporation...
