Comment by kaycebasques
4 days ago
I've also been debating this: https://technicalwriting.dev/ai/agents/#gotta-keep-em-separa...
(Quoting from that post)
Arguments in favor of keeping them separated:
* Writing style. In agent docs, using all caps might be an effective way to emphasize a particular instruction. In internal eng docs, this might come off rude or distracting.
* Conciseness vs. completeness. In agent docs, you likely need to keep the content highly curated. If you put in too much content, you’ll blast through your API quotas quickly and will probably reduce LLM output quality. In internal eng docs, we ideally aim for 100% completeness. I.e. every important design decision, API reference, workflow, etc. is documented somewhere.
* Differing knowledge needs. The information that LLMs need help with is not the same as the information that human engineers need help with. For example, Gemini 2.5 Pro has pretty good built-in awareness of Pigweed’s C++ Style Guide. I tested that assertion by invoking the Gemini API and instructing it Recite the Pigweed C++ Guide in its entirety. It did not recite in full, but it gave a detailed summary of all the points. So the Gemini 2.5 Pro API was either trained on the style guide, or it’s able to retrieve the style guide when needed. Therefore, it’s not necessary to include the full style guide as AGENTS.md context. (Credit to Keir Mierle for this idea.)
Arguments against:
* Duplication. Conceptually, agent docs are a subset of internal eng docs. The underlying goal is the same. You’re documenting workflows and knowledge that’s important to the team. But now you need to maintain that same information in two different doc sets.
> Writing style. In agent docs, using all caps might be an effective way to emphasize a particular instruction. In internal eng docs, this might come off rude or distracting.
To pile on to this, an agent needs to see "ABSOLUTELY NEVER do suchandsuch" to not do suchandsuch, but still has a pretty fair chance of doing it by accident. A talented human seeing "ABSOLUTELY NEVER do suchandsuch" will interpret this to mean there are consequences to doing suchandsuch, like being fired or causing production downtime. So the same message will be received differently by the different types of readers.
Negative assertions can lead to unwanted weights in the context.
I've found positive assertions to be more predictable.
This. When doing Stable Diffusion, I have noticed this as well. Adding negatives can sometimes lead to the opposite results.
From what I can tell, if you say "no computers" for example (ie adding computer as negative), you are setting the scene for something like "where there should be computer, there is not".
I can't better describe this phenomenom, only that it can completely change the output in unexpected unwanted ways.
AB - B = AC
Do you mind sharing a specific concrete example? I'm curious.
1 reply →
Using all caps will actually cause GPT-5 to not function effectively. Have a look at the GPT-5 tuning documentation for coding.