Agentic Skill Decay: Why Your AI Agents Get Worse Over Time
Agentic AI systems drift in capability long before they fail outright. This post breaks down the mechanisms, the production signals, and the engineering practices that keep agents sharp.
Production Ai
Learning AI Engineering from Scratch in 2026: A Production-First Roadmap
Skip the tutorials and learn AI engineering the way it actually ships in production: pick a stack, build small, evaluate honestly, and deploy behind an API. Here’s the roadmap.
The Shift from RAG to Agentic Memory: Optimizing Long-Context LLMs for Production Workflows
Introduction The past few years have witnessed an explosion of interest in retrieval‑augmented generation (RAG) as a way to overcome the limited context windows of large language models (LLMs). By pulling relevant documents from an external datastore at inference time, RAG can inject up‑to‑date knowledge, reduce hallucinations, and keep token usage low. However, as LLMs grow from research curiosities to core components of production‑grade workflows, the shortcomings of classic RAG become increasingly apparent: ...
Detailed Metrics for Evaluating Large Language Models in Production: A Comprehensive Guide
Large Language Models (LLMs) power everything from chatbots to code generators, but their true value in production environments hinges on rigorous evaluation using detailed metrics. This guide breaks down key metrics, benchmarks, and best practices for assessing LLM performance, drawing from industry-leading research and tools to help you deploy reliable AI systems.[1][2] Why LLM Evaluation Matters in Production In production, LLMs face real-world challenges like diverse inputs, latency constraints, and ethical risks. Traditional metrics like perplexity fall short; instead, use a multi-faceted approach combining automated scores, human judgments, and domain-specific benchmarks to measure accuracy, reliability, and efficiency.[1][4] ...
Ultrathink: A Guide to Masterful AI Development
Introduction Ultrathink is not a methodology—it’s a philosophy of excellence in software engineering. It’s the mindset that transforms code from mere instructions into art, from functional to transformative, from working to inevitable. In an era where AI can generate code in seconds, the differentiator isn’t speed—it’s thoughtfulness. Ultrathink is about taking that deep breath before you start, questioning every assumption, and crafting solutions so elegant they feel like they couldn’t have been built any other way. ...