Prompt injection remains the most effective way to compromise enterprise AI systems because it exploits the fundamental way ...

Moving forward requires coordinated technical, policy, and educational responses. An outright ban on AI in peer review, as is ...

The days of simply hoping to rank through passive optimization for opaque algorithms have officially come to an end and the ...

Learn how to evaluate LLM quality and limitations using a range of testing techniques, from unit and regression testing to ...

After months of testing local LLMs, I found that productivity depends on tools, not just models.

Many say that generative AI only produces bland homogenized slop. This overlooks the use of good prompts. Prompt your way to ...

The model learns that hedging is a signal of lower-quality output. This creates a systematic bias toward sounding certain.

Pilots that looked promising do not always survive the transition, and the failure pattern is consistent enough that data leaders can plan around it. This article describes three failure modes that ...

This is the 2nd part of my analysis on Anthropic Claude and its system-wide prompt, focusing on the mental health directives.

Look to these key metrics and benchmarks to evaluate the performance, capability, reliability, and safety of your AI models ...

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a ...