Generative AI Insights, an InfoWorld blog open to outside contributors, provides a venue for technology leaders to explore and discuss the challenges and opportunities presented by generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content.
How can enterprises secure and manage the expanding ecosystem of AI applications that touch sensitive business data? Start with a governance framework.
Failed AI projects waste time and resources, damage reputations, and stifle innovation. To succeed with AI, put the necessary practices in place to ensure high-quality data.
The potential for generative AI to deliver a significant return on investment is being demonstrated by early adopters across various industries. My company provides one example.
By giving developers the freedom to explore AI, organizations can remodel the developer role and equip their teams for the future.
Python developers are uniquely positioned to succeed in the AI era, with a little help from upskilling.
SB 1047 missed the mark. A far better solution to managing AI risks would be a unified federal regulatory approach that is adaptable, practical, and focused on real-world threats.
How high-quality, synthetically designed data sets enable the development of specialized AI models.
Five of the most common and complex challenges organizations face in putting large language models into production and how to tackle them.
Combining knowledge graphs with retrieval-augmented generation can improve the accuracy of your generative AI application, and generally can be done using your existing database.
LLMs are powering breakthroughs and efficiencies across industries. When choosing a model, enterprises should consider its intended application, speed, security, cost, language, and ease of use.
Once you get your retrieval-augmented generation system working effectively, you may face new challenges in scalability, user experience, and operational overhead.
Both the US and the EU have mandated a risk-based approach to AI development. Whatever your risk level, ultimately it’s all about transparency and security.
Understanding the lumpy pattern of technological evolution is essential for organizations that want to make informed decisions about when to invest in and adopt new technologies.
Any leading large language model will do. To succeed with retrieval-augmented generation, focus on optimizing the retrieval model and ensuring high-quality data.
A modern AI-enabled iPaaS solution that supports collaborative workflow design and management can break down silos between IT and business teams and propel automation initiatives forward.
For IT admins, engineers, and architects, language models will save time and frustration and increase confidence in troubleshooting, configuration, and many other tasks. Here are six ways they’ll make operations easier.
With the rise of AI-generated code, development teams must become smarter about how they conduct code reviews, apply security tests, and automate their testing.
Just as AI-powered programming assistants make developers more productive, AI will streamline workflows for data analysts. It will also bring vast benefits to business users.
By integrating domain-specific data, RAG ensures that the answers of generative AI systems are richly informed and precisely tailored. More sophisticated techniques are on the horizon.