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The AI Agent Security Playbook
AI agents create attack surfaces that chatbots don't. This playbook covers prompt injection, tool misuse, data exfiltration, multi-agent attacks, defense-in-depth, and the compliance timeline.
Fine-Tuning vs RAG vs Prompt Engineering: A Decision Framework
Every AI builder hits the crossroads: better prompts, retrieval, or fine-tuning? This guide provides a concrete decision tree based on data freshness, accuracy needs, cost, and latency.
How to Evaluate AI Models Without Trusting Benchmarks
Benchmarks are contaminated, gamed, and misleading. Here's how to build evaluation systems that predict real-world model performance.
The True Cost of Running AI Agents in Production
Raw API pricing is 30-50% of total agent cost. This guide breaks down where the money actually goes, from orchestration overhead to the Jevons paradox, and how to cut spend without cutting capability.
AI Alignment Explained: What It Actually Means to Make AI Do What We Want
What AI alignment actually means as an engineering problem. The three core challenges, the techniques that exist today, and why agents make everything harder.
Chain-of-Thought Prompting: When It Works, When It Fails, and Why
Chain-of-thought is the most studied prompting technique in AI, and the most misapplied. A decision framework for when it helps, when it hurts, and what it costs.
How to Read AI Research Papers Without a PhD
A practical guide to reading AI research papers. Learn the three-pass method, spot red flags in benchmarks and methodology, and build a sustainable reading practice.