Human‑like LLM Output Is a Design Trap: Why Compression Hides Critical Failures
The post argues that prompting LLMs to produce “human‑friendly” language—short sentences, simplified technical English—forces lossy compression of the model’s raw output, obscuring vital error signals. When agents talk to each other, this “humanisation” hides failures and makes debugging harder. The author suggests keeping high‑fidelity data internally and only summarizing for the end user.