Category: Artificial Intelligence

Learn how to evaluate large language models with a practical, real-world benchmarking framework that goes beyond misleading public scores. Discover domain-specific tests, contamination checks, and dynamic evaluation methods that actually predict performance.

Prompt chaining breaks complex AI tasks into reliable steps, reducing hallucinations by up to 67%. Learn how to design effective chains, avoid common pitfalls, and use real-world examples from AWS, Telnyx, and IBM.

In 2025, choosing between API and open-source LLMs isn't about which is better-it's about cost, control, and use case. Learn where each excels and how to pick the right one for your needs.

Learn how model compression techniques like quantization, pruning, and knowledge distillation make large language models faster, cheaper, and deployable on everyday devices-without sacrificing too much accuracy.

Discover how data balance and optimal sampling ratios, not raw volume, drive performance in multilingual LLMs. Learn why proportional training fails and how the latest scaling laws enable equitable AI across low-resource languages.

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.