CRESS Principles for Context Engineering – E is for Empirical

Most commercial LLMs – that is to say, the ones with expensive lawyers – display a disclaimer along the lines of “<LLM> can make mistakes. Check important info.” They’re not kidding. Every token of text an LLM generates should be considered suspect, and when fidelity matters, we really should check the output thoroughly. In programming, fidelity matters. I appreciate that’s the kind of heresy that can get you sacked in Silicon Valley these days, where YOLO – mostly driven by FOMO – dominates. But in banks and retail chains and hospitals and payroll it really does still matter – which is why, on high-risk systems, applying LLM-generated code changes directly is effectively banned in many organisations. And it’s a two-way street. If we want more trustworthy output from an LLM, we need it to have more trustworthy input – both in training and in inference. As users, we don’t have any control over the quality of the data an LLM is trained on, but we do have control over the quality of the…