Shibl's Blog
My Generative AI Development Lifecycle
Over the past two years, my development process for LLM-based AI solutions has become a clear and repeatable method, a reliable blueprint I consistently use. It begins with a precise Prompt, followed by analyzing results through thorough Evaluation, then integrating and deploying in real-world scenarios. From there, I observe AI in action via LLM observability, gathering insights that inform the final stage "Revisit & Improve" where each iteration boosts the solution’s performance.
Why Writing Good AI Evaluations is So Damn Hard and So Damn Essential
Developing powerful AI systems is just half the battle—evaluating them effectively is equally critical, yet notoriously difficult. AI evaluations often suffer from gaps between technical expertise and business insight, as well as from the vast variability in use cases. Learn why robust AI evals matter, why they’re challenging to get right, and how investing early, iterating continuously, and seeking diverse input can lead to AI solutions that are both technically sound and strategically valuable.