Do you have to teach an AI to tell uncomfortable truths?

Why hard facts lead to better decisions

This stance has an older root for me. In 2019 I call in a video for a code of ethics for programmers, the way doctors and teachers have one, because moving fast and breaking things does not work once you are experimenting on real people. The same conviction sits today in the rule to put hard facts before a pleasant answer. Anyone who takes people seriously with technology owes them the truth.

That is why my core instructions state it plainly: hard facts before a pleasant answer. A softened version of the truth feels better in the moment. It is worth nothing for a decision, because the decision has to rest on the real situation. We humans do the same thing on a smaller scale. Confabulation describes exactly that reflex: supplying a fitting reason afterwards and then believing it yourself.

I want to hear the hard facts and the hard things, because making good decisions matters more to me.

The lines that make an AI more honest

These lines sit in my permanent core instructions, the field Codex calls personalisation. Anyone who wants them can take them over almost word for word.

Two additions work noticeably on top of that: give standards and give reasons. Tell the AI why you want a thing done this way. In my experience that improves results enormously, as if it understood more deeply what sits behind the task. That is why the rule lives in the context the AI has in front of it for every answer. Newer models need less polished instructions and more real access.

• I would rather have an uncomfortable truth than a pleasant adjustment of the truth.
• In every important answer, separate: verified, strong conclusion, uncertain, unknown, blocked.
• Name the source or the visible evidence.
• Say explicitly when you do not know something.
• Never mistake a plan, a draft or one successful partial check for a finished result.

Your own writing style as a counterweight

The same softening sits in the writing. Against it I keep a dedicated skill that checks a text does not sound like AI. More important than the skill is the insight behind it: you have roughly twenty writing styles. One for LinkedIn, one for each kind of email, depending on who wrote to you and how that person writes. The AI has to learn those twenty, ideally from your last hundred texts. One of the styles in the collection is my personal one, and it sits publicly on the resource page of the session.

What this means for a department

A department preparing drafts for you faces the same choice. An agent that only writes what sounds good is useless for approval. It has to show which claim is backed by evidence and where a statement is still open. That is exactly what the guardian of the brand checks before a human even sees the draft. In the mandate the case-study reports on, this turned into a procedure: every piece of feedback became a fixed rule, and the agents have written in the language of the brand with evidenced statements ever since.

Two things keep that honest once the number of drafts grows. First, transparency. On one AI-led blog I list the quality criteria openly, together with the condition for when a post may be published. That takes noticeable pressure out of the project. Second, a second pair of eyes. In almost every project another person looks over the finished draft, tells me what to change, and gets the revised version back. Why your own judgement is not enough for this is in Why looking inward is not enough. How to receive that kind of feedback is in When is criticism a gift. And how to get the AI to ask the right questions first is in Should the AI interview you.

From the KI DeepDive

KI DeepDive Agentic Mindset, public live session, 3 August 2026, together with the prompt pack of the session.

• A line in the core instructions: hear an uncomfortable truth rather than an adjusted version of it
• The same tendency toward a softer reading also observed in the writing style and actively corrected
• Hard facts explicitly placed above a pleasant answer, because good decisions matter more
• Five levels in every important answer: verified, strong conclusion, uncertain, unknown, blocked
• Giving standards and reasons improves results markedly, by direct experience
• In almost every project a second person also checks the finished draft

Sources and links

This note keeps growing

2026-09-03: Deepened with a video of my own from 2019: the call for a code of ethics for programmers as the older root of the truth-before-reassurance rule.

2026-09-03: Deepened: the five lines from the prompt pack, standards and reasons as amplifiers, the twenty writing styles, the example from the running mandate, the second pair of eyes, source list.

2026-09-02: Planted from the KI DeepDive Agentic Mindset.