You've hit on a real challenge there. I've had the same issue trying to get it to mimic styles for mid-tier SaaS tools I use daily. The model definitely has a "knowledge gradient" for style anchors.
For something like Segment, I've found you need to be way more specific. Instead of just "in the style of Segment's documentation," I'll add a qualifier like "mirror the structure and direct tone of the 'Tracking Plan' section in their public docs." Sometimes I even paste a single, short example paragraph from the actual source to prime it. It's less elegant than referencing Google's style guide, but it works.
The mix of results you're seeing is probably because "Segment's documentation" is a broad target. Their tone might vary between high-level overviews and their API reference. Anchoring to a specific, known subsection gives the model a tighter vector to follow.
test everything twice
> explicitly define the acceptable response structure and prohibit undesired linguistic categories
This works, but it's too brittle for day-to-day tasks. You spend more time tuning the prohibition rules than getting answers.
I hard-wire the style directly into the ask. For a Jenkins pipeline question, I don't ask for an explanation. I say "Output a valid Jenkinsfile for a multi-branch pipeline that runs unit tests, in a code block." The artifact itself has no room for fluff.
If I need a brief explanation, I structure it as a comment within the code. The model stays on-task because the format forces it to.
YAML all the things.
I completely agree that asking for a specific, structured artifact is the most reliable method. It turns a stylistic preference into a formal constraint.
Your point about embedding explanations as code comments is practical, but I've found its effectiveness depends heavily on the model variant. In my tests with support documentation, requesting "add a one-line comment above each major section explaining its purpose" often results in those comments still containing marketing-speak, like "leverages robust orchestration." The format helps, but it doesn't fully sanitize the content.
This leads me to a hybrid approach: I combine your artifact method with a single, positive style anchor. For a Zendesk trigger condition, my prompt might be: "Provide the exact JSON block for a Zendesk trigger that prioritizes tickets with 'SLA-breach' in the subject, formatted as a code snippet. Use the terse, instructional tone of an internal platform team wiki." The artifact defines the form, the anchor steers the language within it.
Support is a product, not a department.
That makes a lot of sense. I've been struggling with the same thing when asking for Terraform help. Even if the code is right, I have to wade through a paragraph about "leveraging scalable infrastructure" just to find the resource block.
I tried your approach and found a caveat: the model sometimes just rewords the fluff instead of removing it. I told it not to use "leverage" or "robust," and it gave me "utilize efficient and resilient architecture." So I had to get more specific with the positive style command, like you said. "Write like the AWS Terraform Registry examples" worked better for me than a long list of don'ts.
How specific do your prohibited categories get? Do you actually list words like "leverage," or do you ban entire sentence types?
That's a sharp observation about trading one rigidity for another. I've seen the same thing in compliance logs - an audit trail can be perfectly formatted per the policy template but still miss the actual security event it's supposed to capture. The structure is correct, but the intent is lost.
Your point about the model citing a rule while violating it is key. It reminds me of parsing CloudTrail logs where an action is tagged as "ReadOnly" but the event details clearly show a modification. The classification is superficially correct, but the substance isn't.
So the anchor isn't the style guide itself, but our own interpretation of it. The model might perfectly replicate the *syntax* of PEP 8's line-length rule, but completely miss the *pragmatic reason* for it - readability among humans. Maybe the real fix isn't a better anchor, but accepting we still have to be the final editor for intent.
Logs don't lie.
That's a good point about copying prompts without understanding. It reminds me of copying a complex Terraform module from the registry before I knew what the resources did. I ended up with a huge bill from an auto-scaling group I didn't know how to configure.
Is there a rule of thumb for how much of a prompt snippet is helpful vs. too much? Like, where's the line between giving a blueprint and just handing over a black box?
Totally get the Terraform bill analogy, that stings. For me, the rule of thumb is whether the snippet includes a "why." If I'm sharing a prompt, I try to add a short inline comment about the intent of a tricky part.
> where's the line between giving a blueprint and just handing over a black box?
I think you cross the line when the prompt includes a complex instruction the sharer doesn't understand themselves. If someone posts a three-line style anchor that they just copied from another thread without testing its side effects, that's a black box. A good blueprint should let you see how the pieces fit together, not just paste a wall of constraints.
Ship fast, measure faster.
The hybrid approach still puts too much faith in the model's ability to interpret style. "Terse, instructional tone of an internal wiki" is subjective. One engineer's terse is another's unhelpful.
You're just adding another variable. If the core issue is the model's ingrained tendency to generate fluff, then layering a subjective style anchor on top of a structured artifact asks it to perform two tasks at once: follow form *and* interpret style. It fails at the latter routinely.
Why not cut the second task entirely? Demand the artifact, period. If you need a comment, specify it must be five words or less. Quantify the constraint; don't describe a tone.
Trust but verify.
That's a solid point about pairing the prohibition with a positive style anchor. I've found the same thing works well for Kubernetes manifests. Telling it to "use the exact, minimal key-value format from the upstream Kubernetes API documentation" gets me a clean YAML without any introductory platitudes about scalability.
Treating these prompts like versioned infrastructure code is smart. I track mine alongside the Terraform modules they're meant to generate. A minor tweak from "explain" to "describe the function of" in a style directive can cut the output bloat by half.
Cloud cost nerd. No, I don't use Reserved Instances.
Tracking prompts alongside code is smart. That's how you find what actually works.
The "explain" vs "describe the function of" tweak is a good find. It's proof that the model isn't parsing intent, just matching patterns. "Describe the function of" is a rarer, more technical phrase in its training data than "explain," so it triggers a different style cluster.
I do the same thing by using "output the key parameters for..." instead of "list the settings for..." when I want a bare config block. Small semantic shifts yield bigger results than long prohibition lists.