Generative AI

While traditional RAG relies on static vector lookups that often lose global context, agentic search transforms retrieval into a dynamic, reasoning-driven process. By utilizing hierarchical structures like RAPTOR, Knowledge Graph RAG and autonomous sub-agents, these systems can navigate complex, multi-hop queries that typically overwhelm standard semantic search. This shift from one-shot retrieval to iterative loops allows for parallelized processing and self-correction, ultimately providing the precision and structural awareness required for professional-grade document analysis. ...

As Large Language Models (LLMs) transition from simple chatbots to autonomous agents, the methods we use to feed them data must evolve. While Retrieval-Augmented Generation (RAG) remains the industry standard for grounding models in external data, its “vanilla” implementation—converting text chunks into vectors for semantic lookup—often falters when faced with interconnected documents, technical jargon, or multi-hop queries. For Machine Learning Engineers (MLEs) and Product Managers (PMs), understanding the shift toward Agentic Search is critical. This approach moves away from static lookups toward dynamic, iterative, and hierarchical strategies that mirror how a human expert navigates a complex knowledge base. ...

Inside Claude Code: How Sub-Agents and Parallel Execution Define Next-Gen Coding Agents Introduction: The Evolution of Coding Agents Coding agents represent a fundamental shift in how developers interact with their codebases. Unlike traditional autocomplete tools or simple code generation models, modern coding agents operate autonomously across multiple files, maintain context over extended sessions, and can break down complex tasks into manageable subtasks. These systems leverage Large Language Models (LLMs) in sophisticated agentic loops where the model can call tools, observe results, and iteratively work toward task completion. ...

Picture this: generating web or phone apps is no longer a daunting task - you can simply describe your desired functionality in plain English and watch as lines of high-quality code are generated before your eyes. The ability to understand, learn and create code using cutting-edge code Generative AI (GenAI) tools has far-reaching implications, such as dramatically reducing time and effort required for software development, allowing developers to spend more time on the more creative aspects of coding. Instead of manually researching how to use various libraries, developers can manage multiple AI bots that perform coding tasks for them using powerful Large Language Models (LLMs) built on state-of-the-art deep learning techniques and trained on vast datasets. With the ability to convert human language into optimized, high quality code with astonishing accuracy and speed, the future of coding looks incredibly bright and filled with amazing innovation. ...

What is GenAI? Generative AI (GenAI) empowers end-users to generate content, such as images and text, quickly and easily. Entrepreneurs are taking advantage of this technology to create a growing number of startups that utilize GenAI models for various aspects of content creation. In the coming year, we can expect to see a proliferation of new products that build on GenAI models like titans GPT-3 and Stable Diffusion. The GenAI renaissance is just beginning and the recent boom in niche end-user applications for this technology is just the tip of the iceberg. These models will serve as the foundation for many future applications ushering in a new GenAI-economy replete with add-ons to existing software and entirely new offerings for end-users. With GenAI, the possibilities for content creation are endless and entrepreneurs are poised to capitalize on this powerful technology to revolutionize the way we create and consume media. ...