Artificial Intelligence has been around for decades, but in the past few years, excitement around it has surged. AI Chatbots are carrying on human-like conversations, AI is generating art, and AI models are predicting what you will buy before you even know you want it. Businesses see the massive potential of AI and are naturally…

Many businesses are still thinking about AI as a way to enhance existing processes, but AI has matured beyond just another tool for automation or efficiency. A much larger transformation is taking place: AI agents are becoming the primary way businesses, customers, and employees interact, shifting the foundation of business operations from human-driven workflows to…

Many businesses see AI agents as tools to automate existing processes, but this approach limits their potential. It’s like strapping a jet engine onto a horse-drawn carriage. The carriage moves faster, but it is still following the same old path. The real value of AI agents isn’t in making outdated systems more efficient, but in…

Artificial intelligence is poised to take a bold step forward in the coming year. While today’s systems excel in handling specific tasks, the next wave of innovation will focus on collaboration. Multi-agent AI, where specialized systems work together to solve complex problems, is emerging as the natural progression of AI capabilities. Although this vision isn’t…

We’re starting to see AI transition from simple request-response interactions to a more dynamic and action-oriented paradigm. This new generation of agentic systems will soon operate with a rich contextual understanding, enabling conversational, context-aware interactions. These systems won’t merely process inputs; they will analyze the situation, draw insights from comprehensive contextual graphs, and autonomously take…

As we close out 2024, it’s clear this was the year when enterprises got serious about AI, but not without missteps. The excitement around generative AI was hard to miss. However, many of us quickly realized that jumping straight to Gen AI applications without a solid data foundation and a clear AI strategy didn’t end…

Knowledge graphs are quickly evolving how we use large language models (LLMs). Traditional retrieval-augmented generation (RAG) helps by connecting models to external data sources so they can pull in relevant information. But there’s a catch: traditional RAG isn’t perfect. It can pull outdated, inconsistent, or irrelevant data, which leads to problems like hallucinations and inaccurate…

Retrieval-augmented generation (RAG) has proven to be a powerful method for connecting large language models (LLMs) to external data sources. By dynamically retrieving information from documents, databases, or APIs, RAG can enhance the relevance of a model’s output. However, traditional RAG isn’t without its limitations; it can expose systems to risks like hallucinations or outdated…