Operations | Monitoring | ITSM | DevOps | Cloud

Generative AI has already shown its huge potential, but there are many applications that out-of-the-box large language model (LLM) solutions aren’t suitable for. These include enterprise-level applications like summarizing your own internal notes and answering questions about internal data and documents, as well as applications like running queries on your own data to equip the AI with known facts (reducing “hallucinations” and improving outcomes).

Organizations saw a 243% ROI and $1.2 million in savings over three years In today’s complex and distributed IT environments, traditional monitoring falls short. Legacy tools often provide limited visibility across an organization’s tech stack and often at a high cost, resulting in selective monitoring. Many companies are therefore realizing the need for true, affordable end-to-end observability, which eliminates blind spots and improves visibility across their ecosystem.

Top tips is a weekly column where we highlight what’s trending in the tech world and list ways to explore these trends. When you think about generative AI, what instinctively comes to your mind is content and image generation. But, in this week’s Top tips column, let’s look at a less-explored facet of generative AI: data analytics. There are a lot of conversations about data and its benefits.

In the ever-changing field of artificial intelligence, OpenAI is consistently seen as a leader in innovation. Its AI models, starting with GPT-3 and now with GPT-4, are already used extensively in software development and content creation, and they’re expected to usher in entire sets of new systems in the future.