article

ai

The Evaluation Framework That Actually Predicts LLM Production Performance

Perplexity is a terrible proxy for production usefulness. A model with excellent perplexity on a held-out text corpus can still produce confidently wrong answers, miss edge cases in your specific domain, or fail at the structured output requirements your application depends on. Teams that select models by benchmark performance and

ai

Building a Content Pipeline with n8n and a Local LLM

The default AI content pipeline assumption is that you need cloud API access to do anything useful. That assumption is wrong, and increasingly expensive to maintain as you scale content volume. A local LLM running on commodity hardware, combined with n8n for orchestration, handles the majority of structured content automation

ai

Why Most RAG Implementations Fail in Production

Retrieval-Augmented Generation is one of the most deployed LLM patterns in enterprise AI, and one of the most frequently broken in production. The demos work. The proof of concept looks promising. Then the system goes live and retrieval quality drops, answers become unreliable, and the team either spends months debugging

ai

The Prompt Engineering Mistake That Costs You 40% of Model Performance

Most teams adopting LLMs in production hit a ceiling at around 60 to 70 percent of the model's actual capability. They assume the gap is a model limitation. Usually it is a prompting limitation: three structural errors that compound on each other: undefined output format, absent system context,