Blogs

Struggling to scale GenAI beyond pilots? Learn how enterprises move from proof-of-concept to production with governance, integration, and ROI-driven AI frameworks.

Learn how agentic AI turns generative AI ideas into action—boosting efficiency, automating workflows, and transforming enterprise operations with autonomous agents.

Discover how hybrid human–AI models transform customer service by combining AI efficiency with human empathy for better outcomes and satisfaction.

Discover how Large Language Models (LLMs) power Generative AI systems, driving intelligent automation, scalability, and enterprise transformation.

Learn why explainability and transparency in Generative AI are essential for finance, healthcare, and insurance. Discover how GenAIinabox.ai delivers safe, auditable, and responsible AI systems.

Learn how Retrieval-Augmented Generation (RAG) blends generative models and real-time data to deliver more accurate and context-aware AI responses.

Explore how multimodal generative AI systems are redefining digital communication. Discover real-world use cases in healthcare, retail, education, and more with GenAI-in-a-Box.

In a world where data privacy, compliance, and scalability are non-negotiable, Generative Adversarial Networks (GANs) are changing the game. Discover how leading industries—from healthcare to finance—are using GAN-generated synthetic data to unlock real-time analytics, protect privacy, and drive innovation.

The quality of your AI output is only as good as the input you provide. If youve ever used a generative AI tool and received a vague or underwhelming response, youre not alone. But the issue likely isn’t with the AI—it’s with the prompt.

Fine-tuning embedding models is a game-changer for improving retrieval performance and ensuring context-aware outputs with lesser latency.

Meet Agentic RAG—where Retrieval-Augmented Generation meets agent-like decision-making, powered by reinforcement learning.

In Retrieval-Augmented Generation (RAG), accurate and relevant information retrieval is crucial for generating high-quality responses. However, traditional retrieval methods often return results that are not optimally ranked for relevance. This is where **reranking** comes into play, significantly improving retrieval system performance.

Embedding Model converts texts, words, images into numerical form known as vectors, Vectors are used for Context and Relationships between texts, words, they are stored in Vector Database.

The effectiveness of a RAG system heavily depends on one fundamental preprocessing step: chunking.

From generating human-like text to automating customer support and assisting with research, LLMs are changing the way businesses and individuals access and process information.

PibyThree prioritizes security and compliance by integrating industry-best practices into every aspect of its GenAI applications.

Our tailored AI services focus on real-world applications, ensuring that businesses unlock the full potential of GenAI for sustained efficiency and innovation.

This article breaks down the fundamental differences between AI and Generative AI, exploring how they work, their applications, and their impact on various fields.

Generative AI (GenAI) is often discussed in the context of Retrieval-Augmented Generation (RAG), a method that enhances AI responses with real-time data. While RAG is a powerful technique, it is far from the full picture of what GenAI can do.

In a hyper-competitive world where every moment counts, businesses are turning to AI and cloud technologies to deliver faster, smarter, and more personalized experiences.

What comes to mind when you hear "generative AI"? Probably ChatGPT crafting a quick reply or drafting an email, right?

Every year, millions of patients wait—sometimes in vain—for life-saving medications that take too long to develop.