Category: Artificial Intelligence - Page 7

Query decomposition breaks complex questions into smaller parts for LLMs to answer step by step, boosting accuracy by over 50%. Learn how it works, where it shines, and whether it’s right for your use case.

Most AI ethics frameworks are just buzzwords. Learn the five measurable principles that actually prevent harm from generative AI-and how to implement them in your organization today.

AI auditing is now mandatory for businesses using AI in hiring, lending, or healthcare. Learn exactly what logs, prompts, and outputs you must track in 2025 to stay compliant and avoid massive fines.

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.

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.