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.

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.

It’s hard not to speak in cliches when we talk about artificial intelligence (AI). Today, AI seems to be all around us. And whatever its cultural impact, its rapid evolution is leading to widespread adoption across industries. Much of the discourse focuses on what machine intelligence can do to enrich our lives and businesses. But less has been said about data, and how every AI system relies on it to operate.

For the past two years, AI enthusiasts have been treated to a nonstop procession of lightspeed innovations. ChatGPT rang the era in with a bang; millions of blogs, emails, trivia questions, and sonnets later, Microsoft etched the writing on the wall with its $10 billion investment in OpenAI, ChatGPT’s progenitor. Since then, companies from Alphabet to Zoom have scrambled to somehow, some way, integrate AI into their core offerings.

The demand for LLM is rapidly increasing—it’s estimated that there will be 750 million apps using LLMs by 2025. As a result, the need for LLM observability and monitoring tools is also rising. In this blog, we’ll dive into what LLM monitoring and observability are, why they’re both crucial and how we can track various metrics to ensure our model isn’t just working but thriving.