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Read how we set up an experimental chatbot environment that allows us to switch LLMs dynamically and enhances the predictability of AI-assisted features' behavior within the ilert platform. The article includes a guide on how you can build something similar if you plan to add AI features with a chatbot interface to your product.

Generative AI (GenAI) is still in its infancy, but its impact is already being felt across industries. Over the past year, production applications leveraging GenAI have gone from proof-of-concept to delivering real-world value. According to the World Economic Forum, 75% of surveyed companies plan to adopt AI technologies by 2027. Leading cloud providers like AWS are making significant investments.

As teams adopt Kubernetes at greater scale, they face increased complexity in keeping their growing list of workloads and services up and running. Achieving the visibility and context needed to detect and resolve incidents quickly is difficult amid a constant flood of telemetry data and alerts. Furthermore, Kubernetes expertise often remains siloed in DevOps and infrastructure teams.

LLMs like GPT-4, Claude, and Llama are behind popular tools like intelligent assistants, customer service chatbots, natural language query interfaces, and many more. These solutions are incredibly useful, but they are often constrained by the information they were trained on. This often means that LLM applications are limited to providing generic responses that lack proprietary or context-specific knowledge, reducing their usefulness in specialized settings.

We all know that AI is becoming as integral to our lives as the internet. Regardless of how you use it, whether for work or just for fun, it is a decent tool for getting information and ideas quickly. Whichever one you use, you can ask it for whatever you want without searching through dozens of web pages that are often filled with ads or data trackers. That being said, there are still privacy concerns regarding AI, so while we will look at ChatGPT vs.

We’re excited to reveal our latest effort towards simplifying and accelerating the test data management process: AI Synthetic Data Generation, part of Redgate Test Data Manager. Officially introduced in a session at the recent PASS Data Community Summit, the capability uses machine learning to rapidly generate realistic yet entirely synthetic data – all while maintaining data integrity and with data privacy built-in as priority.

Building an AI proxy for our AI features was one of the best decisions we made a year ago. In this article, we will share why and what challenges we faced.