Pip: Welcome to Azure Advice — where today's question is whether the AI tools we're shipping are useful products or just very confident fiction generators with good PR.
Mara: Christoph Corder has been writing about exactly that tension. This episode focuses on one territory: how hallucinations happen, why they keep happening, and what the engineering decisions look like that actually stop them.
Pip: Let's start with the argument that hallucinations aren't a bug report — they're a design choice.
Hallucinations Are a Design Decision
Pip: The framing here is provocative but precise: most hallucinations that reach users aren't the model's fault. They're the tool builder's fault — for not constraining what the model is asked to do or checking what it returns.
Mara: The post opens with a sharp analogy to set that up. The argument is that warning labels are not the same as engineering, and the post puts it this way: "That's not a disclaimer. That's a confession with better typography."
Pip: And the stakes are real. The New York lawyers sanctioned in 2023 for filing ChatGPT-invented court cases are the example — the 8-point disclaimer didn't protect them, and it won't protect anyone building tools today.
Mara: The post then walks through ten concrete fixes. The organizing principle is that fluency and accuracy are different things, and the tool is responsible for enforcing the difference. One of the clearest rules is: no evidence, no claim. If the model can't point to where something came from, it doesn't ship.
Pip: Which sounds obvious until you realize most prompts don't say that anywhere.
Mara: Right. Another fix is making "I don't know" a valid answer. The post cites an OpenAI research paper from 2025 — "most benchmarks score a lucky guess higher than an honest I don't know, so models learn to guess." If your prompt doesn't allow uncertainty, the model fills the gap.
Pip: So the model isn't broken. It's doing exactly what it was trained and graded to do. The tool just never told it to stop.
Mara: The post also flags a finding called the "Lost in the Middle" study — models get significantly worse at using details buried in long context windows. Whatever they miss, they fill with assumptions. The prescription is to curate what goes in: relevant beats complete.
Pip: Your context window is not a junk drawer. That line is doing a lot of work and earning every word.
Mara: The post closes by borrowing a sixty-year-old problem-analysis framework from Kepner and Tregoe — the idea being that the discipline of separating observation from interpretation, and testing every cause against every fact, works on models for exactly the reason it works on people under pressure.
Pip: AI didn't invent confident wrong answers. It industrialized them. The fix, apparently, was published in 1965.
Mara: The bottom line the post lands on: "Confidence is cheap. Evidence is expensive. Build tools that know the difference."
Pip: None of the fixes require a better model. They require someone who stopped mistaking fluency for accuracy — and that part is entirely on the builder.
Mara: The through-line here is accountability — not for the model, but for the people designing around it.
Pip: Make the AI prove it. That's the ask. See you next time, when we find out what else has been hiding in plain sight.
