This blog explores a comprehensive framework for evaluating generative AI models by focusing on three critical dimensions: accuracy, safety, and fairness, and outlines practical strategies, tools, and best practices to help organizations implement responsible, multi-dimensional assessment at scale.

In this blog, we’ll break down the best practices for synthetic data generation in generative AI and dive into the challenges and best practices that define its responsible use. We’ll also examine real-world use cases across industries to illustrate how synthetic data is being leveraged today. 

This blog explores real-world use cases of RLHF in generative AI, highlighting how businesses across industries are leveraging human feedback to improve model usefulness, safety, and alignment with user intent. We will also examine its critical role in developing effective and reliable generative AI systems and discuss the key challenges of implementing RLHF.

This blog explores the real-world use cases of RAG in GenAI, illustrating how Retrieval-Augmented Generation is being applied across industries to solve the limitations of traditional language models by delivering context-aware, accurate, and enterprise-ready AI solutions.

This blog explores how bias manifests in generative AI systems, why it matters at both technical and societal levels, and what methods can be used to detect, measure, and mitigate these biases. It also examines what organizations can do to mitigate bias in Gen AI and build more ethical and responsible AI models.

This blog breaks down how generative AI models differ in capability, how they scale in enterprise environments, and what trade-offs organizations must consider. We’ll also examine how modern approaches such as Retrieval-Augmented Generation (RAG), fine-tuning, and Reinforcement Learning with Human Feedback (RLHF) influence the overall performance and cost. 

This blog takes a deep dive into three Gen AI fine-tuning techniques: LoRA, QLoRA, and Adapters, comparing their architectures, implementation complexity, hardware efficiency, and real-world applicability. 

This blog explores what Reinforcement Learning with Human Feedback (RLHF) is, why it’s important, associated challenges and limitations, and how you can overcome them.

This blog offers a practical, evidence-backed approach to mitigating bias in GenAI within defense and national security. We will explore how to detect, address, and monitor bias throughout the AI lifecycle. 

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.

In this blog, we will explore synthetic data generation for edge cases in perception AI, exploring its benefits and the different types of synthetic data.