Author: Mark Chomiczewski - Page 2

Rotary Position Embeddings (RoPE) revolutionized how LLMs handle context by encoding position through rotation instead of addition. It enables models to generalize to longer sequences without retraining, making it the standard in Llama, Gemini, and Claude. But it comes with tradeoffs in memory, implementation complexity, and edge cases.

NLP pipelines and end-to-end LLMs aren't rivals-they're teammates. Learn when to use each for speed, cost, accuracy, and creativity-and how top teams combine them to get the best of both worlds.

Caching AI responses can slash latency by 80% and cut costs by 60-70%. Learn how to start with Redis or MemoryDB, choose the right caching type, avoid common pitfalls, and make your AI app feel instant.

Learn how to write prompts that generate clean, documented, and team-friendly code. Stop fixing AI-generated code and start building code that lasts with clear, specific, maintainable prompts.

LLMOps keeps generative AI systems accurate, safe, and affordable. Learn how to build reliable pipelines, monitor performance in real time, and stop model drift before it breaks your app or costs you millions.

Learn how to cut RAG pipeline latency from 5 seconds to under 1.5 seconds using streaming, intent classification, vector database tuning, and connection pooling - critical for production LLM systems.

Truthfulness benchmarks like TruthfulQA reveal how often generative AI models repeat false information. In 2025, top models like Gemini 2.5 Pro score 97% on factual accuracy tests - but real-world use still shows dangerous errors. Here’s how to evaluate and reduce AI hallucinations.

Learn the essential KPIs and dashboard practices for monitoring large language model health in production. Track hallucinations, latency, cost, and user trust to prevent failures and ensure responsible AI.