Category: Technology Engineering

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… Continue reading

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

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… Continue reading

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… Continue reading

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… Continue reading

Introduction: LLM routing and load balancing are critical for building cost-effective, reliable AI systems at scale. Not every query needs GPT-4—many can be handled by smaller, faster, cheaper models with equivalent quality. Intelligent routing analyzes incoming requests and directs them to the most appropriate model based on complexity, cost constraints, latency requirements, and current system… Continue reading

Introduction: Retrieval evaluation is the foundation of building effective RAG systems and search applications. Without proper metrics, you’re flying blind—unable to tell if your retrieval improvements actually help or hurt end-user experience. This guide covers the essential metrics for evaluating retrieval systems: precision and recall at various cutoffs, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative… Continue reading

Introduction: Prompt debugging is an essential skill for building reliable LLM applications. When prompts fail—producing incorrect outputs, hallucinations, or inconsistent results—systematic debugging techniques help identify and fix the root cause. Unlike traditional software debugging where you can step through code, prompt debugging requires understanding how language models interpret instructions and where they commonly fail. This… Continue reading