Author: Mark Chomiczewski

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.

Generative AI demands more than technical skill-it requires ethical responsibility. Learn how stakeholder engagement and transparency build trust, prevent harm, and ensure AI is used fairly in research, education, and beyond.

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.

Learn how axe-core, Lighthouse, and Playwright help catch accessibility issues in modern frontends. Use them together to build apps that work for everyone-not just the majority.

ADR and decision logs are essential for documenting AI-driven system architecture. Learn how AI-assisted documentation reduces confusion, improves onboarding, and ensures compliance with industry standards.

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.