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.