Author: Nithin Mohan TK

Introduction: Knowledge distillation transfers the capabilities of large, expensive models into smaller, faster ones that can run efficiently in production. Instead of training a small model from scratch, distillation leverages the “dark knowledge” encoded in a teacher model’s soft probability distributions—information that hard labels alone cannot capture. This guide covers the techniques that make distillation […]

Introduction: Semantic caching revolutionizes how we handle LLM requests by recognizing that similar questions deserve similar answers. Unlike traditional exact-match caching, semantic caching uses embeddings to find queries that are semantically equivalent, returning cached responses even when the wording differs. This can reduce LLM API costs by 30-70% while dramatically improving response latency for common […]

Introduction: Vector search is the foundation of modern semantic retrieval systems, enabling applications to find similar items based on meaning rather than exact keyword matches. Understanding the algorithms behind vector search—from brute-force linear scan to sophisticated approximate nearest neighbor (ANN) methods—is essential for building efficient retrieval systems. This guide covers the core algorithms that power […]