Artificial Intelligence (AI) is no longer a futuristic concept—it’s already reshaping industries, powering smart assistants, automating tasks, and transforming how we interact with technology. But AI is not a single technology. It’s a vast field composed of different types and categories, each serving distinct purposes and capabilities. So, what are the different types of AI? … Read more
Retrieval-Augmented Generation (RAG) is one of the most powerful techniques used in conjunction with large language models (LLMs) to solve the limitations of fixed, pre-trained models. If you’ve ever wondered “how does RAG work in LLM?”, you’re in the right place. In this post, we’ll break down how RAG works, why it’s useful, and how … Read more
In the rapidly evolving world of artificial intelligence, two branches are making significant waves across industries: Generative AI and Predictive AI. While both fall under the umbrella of machine learning and share certain underlying principles, their goals, methodologies, and applications differ substantially. Understanding the differences between Generative AI vs Predictive AI is crucial for businesses … Read more
The evolution of artificial intelligence has brought about sophisticated systems that merge retrieval and generation capabilities to create powerful, context-aware AI applications. One of the most impactful innovations in this space is the agentic RAG (Retrieval-Augmented Generation) system. If you’re exploring advanced AI architectures or implementing intelligent assistants, understanding the core structure of an agentic … Read more
As organizations explore the advantages of large language models (LLMs), the demand for local deployment is rising. Running an LLM locally gives organizations more control over data privacy, latency, and customization. One powerful use case that is gaining momentum is local LLM database integration. This setup allows locally hosted language models to interact with structured … Read more
With the rapid adoption of large language models (LLMs), many developers are opting to run these powerful tools directly on their own machines. Local deployment offers numerous advantages, including enhanced privacy, reduced latency, and better control over your environment. However, not all LLMs are lightweight, and running them locally requires a clear understanding of your … Read more
As the AI revolution continues to reshape how developers write and understand code, the demand for privacy-conscious, resource-efficient, and powerful tools has skyrocketed. Enter the era of local LLMs for coding. For developers who want to avoid the latency and privacy concerns of cloud-based APIs, choosing the best local LLM for coding is both a … Read more
Retrieval-Augmented Generation (RAG) has been a breakthrough innovation in the evolution of language models. But the latest advancement—Agentic RAG—takes the technology one step further by embedding reasoning, decision-making, and goal-directed behaviors into the retrieval pipeline. This significantly enhances the accuracy, relevance, and depth of AI-generated responses. In this post, we explore how Agentic RAG improves … Read more
Agentic AI is not just a conceptual shift in artificial intelligence—it is a technological leap that enables autonomous, goal-driven behavior across dynamic workflows. This capability makes agentic AI highly suitable for real-world applications where adaptability, decision-making, and multi-step task execution are crucial. But what industries are most likely to benefit from this paradigm shift? In … Read more
Large Language Models (LLMs) are rapidly transforming the way we build intelligent applications. Whether you’re working on customer support bots, search engines, internal knowledge assistants, or even creative content generation tools, you’ve probably encountered two common ways to adapt LLMs to specific tasks or domains: RAG (Retrieval-Augmented Generation) and Fine-Tuning. In this post, we’ll dive … Read more
