Category: Emerging Technologies

Introduction: AI agents represent the next evolution of LLM applications—systems that can reason, plan, and take actions to accomplish complex tasks autonomously. Unlike simple chatbots that respond to single queries, agents maintain state, use tools, and iterate toward goals. This guide covers the architectural patterns that make agents effective: the ReAct framework for reasoning and […]

Introduction: Embeddings are the foundation of modern AI applications—they transform text, images, and other data into dense vectors that capture semantic meaning. Understanding how embedding models work, their strengths and limitations, and how to choose between them is essential for building effective search, RAG, and similarity systems. This guide covers the landscape of embedding models: […]

Introduction: Prompt optimization is the systematic process of improving prompts to achieve better LLM outputs—higher accuracy, more consistent formatting, reduced latency, and lower costs. Unlike ad-hoc prompt engineering, optimization treats prompts as artifacts that can be measured, tested, and iteratively improved. This guide covers the techniques that make prompts more effective: structural patterns that improve […]