In this blog, we will delve into the methodologies and frameworks that practitioners are using to red team generative AI systems. We’ll examine the types of attacks models are susceptible to, the tools and techniques available for conducting these assessments, and integrating red teaming into your AI development lifecycle. 

This blog explores the core components of GenAI model evaluation in simulation environments. We’ll look at why simulation is critical, how to select meaningful metrics, what makes a benchmark robust, and how to integrate human input without compromising scalability. 

In this blog, we'll explore what agentic AI is, examine its capabilities and limitations, and discuss why human-in-the-loop is critical for these AI agents.

In this blog, we will explore Fine-Grained Reinforcement Learning from Human Feedback (Fine-Grained RLHF), an innovative approach to improve language model training by providing more detailed, localized feedback. We'll discuss how it addresses the limitations of traditional RLHF, its applications in areas like detoxification and long-form question answering, and the broader implications for building safer, more aligned AI systems. 

In this blog, we will discuss object detection in surveillance systems and how quantized object detection models are reshaping image categorization. We’ll explore the challenges of categorizing visual data in real-world surveillance environments, define what quantized models are and how they work, and examine the specific advantages they bring to the table.

This blog explores horizontal AI and vertical AI in depth, highlighting their advantages, challenges, and key differences, so you can decide which AI strategy is right for you.

In this blog, we’ll break down what AI hallucinations are, why they happen, how to spot them, and what businesses can do to prevent them.

In this article, we will explore the essential role of human oversight in ensuring the safe deployment of LLMs, highlighting why it is crucial and where it is most needed.

In this blog, we’ll explore advanced fine-tuning techniques that enhance the performance of domain-specific language models. We’ll cover essential strategies such as parameter-efficient fine-tuning, task-specific adaptations, and optimization techniques to make fine-tuning more efficient and effective.

This guide will explore fine-tuning for LLMs, covering key techniques, a step-by-step process, and real-world use cases.