Category: Emerging Technologies

Introduction: LLM latency is the silent killer of user experience. Even the most accurate model becomes frustrating when users wait seconds for each response. The challenge is that LLM inference is inherently slow—autoregressive generation means each token depends on all previous tokens. This guide covers practical techniques for reducing perceived and actual latency: streaming responses […]

Introduction: Agentic workflows represent a paradigm shift from simple prompt-response patterns to autonomous, goal-directed AI systems. Unlike traditional LLM applications where the model responds once and stops, agentic systems can plan multi-step solutions, execute actions, observe results, and iterate until the goal is achieved. This guide covers the core patterns that make agentic systems work: […]

Introduction: Prompt engineering is the art and science of communicating effectively with large language models. Unlike traditional programming where you write explicit instructions, prompt engineering requires understanding how models interpret language, what context they need, and how to structure requests for optimal results. This guide covers the fundamental techniques that separate amateur prompts from production-quality […]

Introduction: Memory is what transforms a stateless LLM into a contextually aware assistant. Without memory, every interaction starts from scratch—the model has no knowledge of previous conversations, user preferences, or accumulated context. This guide covers the memory architectures that enable persistent, intelligent AI systems: conversation buffers for recent context, summary memory for long conversations, vector-based […]