Systems | Development | Analytics | API | Testing

LLMs are the foundation of gen AI applications. To effectively operationalize and de-risk LLMs and ensure they bring business value, organizations need to consider not just the model itself, but the supporting infrastructure, including GPUs and operational frameworks. By optimizing them to your use case, you can ensure you are using an LLM that is the right fit to your needs.

Observability refers to understanding a system’s internal state by examining its external outputs, like logs, metrics, and traces. Borrowed from control theory, it’s the ability to infer what’s happening inside a system without direct access. To visualize observability, consider the human body. We don’t need to open it up to understand its internal state. By measuring external signals like body temperature, heart rate, or blood pressure, we can infer what’s happening inside.

You’ve probably heard of generative AI, which uses deep learning models to simulate human decision-making by processing large amounts of data, and then responding with pertinent new content. Since the arrival of ChatGPT in 2022, generative AI has come a long way — but now there’s another technology on the scene that could further revolutionize the capabilities of AI. Read on to learn more about agentic AI and how it could transform the business landscape.

More than 80% of developers and business leaders say AI investments have already created the opportunity for new products or services, according to Kong’s 2024 API Impact Report. Clearly, AI has proven its value and place in the enterprise, but with new innovations come new potential vulnerabilities. But as organizations push forward into and navigate the rising risk of AI-enhanced threats and the adoption of AI tools and large language models (LLMs), what have tech leaders experienced?

As ThoughtSpot accelerates into its next phase of growth with AI-powered analytics, we are more committed than ever to providing value to our enterprise customers. In recent months, we delivered several features and enhancements designed specifically for your IT and Security Operations Administrators, who are at the forefront of provisioning and managing users and access to business objects.

Sorry for the click-bait title, but everyone is talking about AI Agents, and for a good reason. With the proliferation of LLMs, everyone – from software engineers using LLMs as a coding copilot to people using AI to plan vacations – is looking for new ways to use the technology that isn’t just answering questions or searching knowledgebases.

For years, companies have operated under the prevailing notion that AI is reserved only for the corporate giants — the ones with the resources to make it work for them. But as technology speeds forward, organizations of all sizes are realizing that generative AI isn’t just aspirational; it’s accessible and applicable now.

Artificial Intelligence promises to transform lives and business as we know it. But what does that future look like? The AI Forecast: Data and AI in the Cloud Era, sponsored by Cloudera, aims to take an objective look at the impact of AI on business, industry, and the world at large.

With the rise of AI-driven tools, the debate of ‘ChatGPT vs Copilot’ has become central for developers seeking productivity boosts in coding and testing. In this blog, we will dive into the key features, strengths, limitations, and use cases of these tools to help you understand their differences and make an informed decision on which one suits your programming needs the most. So, let’s get started!