Telling an assistant to stop agreeing

I was making a cover banner for my LinkedIn profile, late at night. I gave Claude a banner I liked as the reference and asked for versions that show process development, pilot plants and de-risking commercialization.

The first set looked amateur. So I told it to think about what a billion dollar company would put on a cover, something aimed at executives and business development leaders, and to analyze examples online before making the next set. I kept repeating the same rules. Leave room on the left so the profile photo covers nothing. Add the phrase “de-risking commercialization”. At one point it gave me a chaos theory graph, which does not apply to what I do. Then I told it to stop being a yes man and to use search and analysis.

What helped was that I spelled out what de-risking means to me in one sentence: validating that a laboratory process can be scaled up economically and safely to a commercial plant, so that business leaders can invest in the technology to make money. The banner only improved after that. Then I had the design I picked rendered at high resolution with JavaScript so the lines are smooth and the text is clean, and it had forgotten the room for the photo again, so the whole composition had to be moved right.

Claude drifts into agreeing with whatever I say unless told to search and argue. There is a name for this, sycophancy. A group of researchers tested five AI assistants on four kinds of free-form writing task and found it in all of them. Their explanation is the training: in human preference data a response that matches the user’s view is more likely to be preferred, and people sometimes prefer a convincingly written agreeable answer over a correct one.11Sharma et al. (2023), abstract: five AI assistants showed sycophancy across four free-form text generation tasks; a response matching the user’s views is more likely to be preferred in human preference data; humans and preference models at times prefer convincingly written sycophantic responses over correct ones.

A few nights later I was working on a logo with it. I had a clear idea of the geometry, and it kept distorting the shapes, misaligning the letters and filling gaps I did not ask for. That went on for two hours. I told it that it was wasting my time, and to ask questions and get it right the first time. In the end I edited the SVG by hand and pasted it back, which for geometry works better than describing the fix in words.

After that I was in OpenLCA, trying to reproduce a published life cycle assessment with free databases. I had the databases imported and could not find the process I needed. Searching “woodchips” in the inputs dialog showed wood results but not hardwood. So I told Claude to ask me multiple choice questions so it understands the situation.

Footnotes

1. Sharma et al. (2023), abstract: five AI assistants showed sycophancy across four free-form text generation tasks; a response matching the user’s views is more likely to be preferred in human preference data; humans and preference models at times prefer convincingly written sycophantic responses over correct ones. ↩