Category: Emerging Technologies

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:… Continue reading

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… Continue reading

Introduction: LLM inference optimization is the art of making models respond faster while using fewer resources. As LLMs grow larger and usage scales, the difference between naive and optimized inference can mean 10x cost reduction and sub-second latencies instead of multi-second waits. This guide covers the techniques that matter most: KV cache optimization to avoid… Continue reading