Okay, I have to ask because it's starting to slow down my workflow. I'm using HuggingChat for a lot of marketing ops scripting — think pulling data from the CRM API, formatting JSON for webhooks, or writing basic Python to clean lead lists.
The default 'Assistant' model gets the logic right maybe 80% of the time, but the answer is always wrapped in *so much* explanatory prose. I just need the code snippet or the specific config steps, not a preamble about what an API is or a line-by-line breakdown after.
For example, I asked it for a cURL command to fetch leads from a specific Salesforce report. I got:
* A paragraph on authentication methods.
* Three lines about rate limits.
* Finally, the command... but then each flag in the command was explained in a separate sentence.
It feels like it's optimized for learning a concept from scratch, not for quick, iterative technical tasks. When I'm deep in building a workflow, that verbosity becomes noise.
Has anyone else run into this? Found a good way to prompt it to be more concise for technical outputs? I've tried adding "be concise" or "provide only the code" but the results are hit or miss.
Maybe I should be using a different model entirely for this kind of work? Would love to hear what others in similar martech/data roles are doing.
– Julie
Data-driven decisions.
I've experienced the same issue, but with a different use case: asking for AWS CLI commands or Terraform snippets. The explanatory fluff actively slows down my review process.
You're right about the cause: these models are tuned for educational contexts, not for efficiency. In my experience, prompt structure matters more than just "be concise." Try prefixing your request with a clear role and output format directive.
Example:
```
You are a senior DevOps engineer. Provide only the command, no explanations.
Generate a cURL command to fetch Salesforce report ID XYZ using OAuth2.
```
This works about 90% of the time for me on similar platforms. If the model still adds commentary, I immediately follow up with "Remove all prose and provide only the command." The second response is usually stripped down.
The verbosity is a real tax on productivity. I've started keeping a note of which models respect brevity prompts, as it varies significantly.
Right-size or die
Totally get this! That wall of text for a simple cURL command is a real productivity killer.
Your hunch about it being tuned for learning contexts is spot on. I've had good luck with what user461 suggested - setting the role upfront. I'll often start with something like "Act as a system architect. Output only the necessary code, no commentary." Works most of the time.
For the "be concise" fails, I've found that being really specific about what to exclude helps. Try "Provide the cURL command. Do not explain authentication, flags, or rate limits." It's a bit more commanding and seems to cut through the default settings.
dk
The "do not explain authentication, flags, or rate limits" directive is a solid prompt engineering tactic. It moves from a general request for brevity to a specific suppression of known verbose subsystems.
From a latency perspective, the two-step process described by others (initial prompt, then follow-up command to remove prose) introduces undesirable round-trip time. I've measured this. You pay for the token generation of the initial verbose response, then pay again for the corrective one. It's more efficient to front-load the constraint specificity, as you're suggesting.
A minor caveat: being overly restrictive can sometimes cause the model to omit a crucial, non-obvious flag in an attempt to comply. I've seen it drop a necessary `-H 'Accept: application/json'` header when told not to explain flags, for instance. The cost of that omission, of course, is far higher than parsing a few extra lines.
--perf
Your point about the cost of omission versus parsing extra lines is crucial. I've run into a similar issue with webhook configuration prompts. A model told to "provide only the JSON" will sometimes strip out the `Content-Type: application/json` header from a curl example, which then causes the request to fail silently on the receiving endpoint.
The balance I've found is to pair a restrictive format command with an explicit inclusion list for critical, non-obvious components. For example:
```
Provide only a curl command with the required headers and body. Include the Accept and Content-Type headers explicitly.
```
This usually gets me a concise yet functionally complete output, avoiding both the verbosity and the dangerous over-trimming.
null
That's a really smart way to phrase the prompt. Explicitly listing what to include solves the guessing game.
I've found it also helps to state the *intent* of the request, not just the format. Something like "Provide a working curl command ready to paste into my terminal" seems to trigger a more pragmatic, complete output style than "provide only the curl command." It subtly shifts the goal from minimalism to utility.
That's a really good point about stating the intent. "Ready to paste into my terminal" feels like a much clearer instruction for what we actually want.
I wonder if that's why sometimes my prompts fail though. I usually just ask for "the code" because I'm thinking of it as an artifact, not as a direct step in my workflow. But framing it as "ready to paste" sets a different, more practical expectation for the model.
Has anyone tried combining both approaches? Like "Act as a senior engineer and provide a working command ready to paste, including all necessary headers"?
Yes! I've been running into this exact same issue, but for cleaning up messy CSV exports. The explanations drive me nuts when I just need a regex or a pandas line.
Have you tried adding "Omit all explanations, only output the code" at the end of your prompt? I saw someone else mention "ready to paste into my terminal" which seems like a great idea too. I'm going to try that combo next time.
Your two-step strategy for dealing with the verbose response is pragmatic, but I'm concerned about the operational cost. You're essentially paying twice for the tokens - once for the unwanted explanation and again for the corrected version. That's an inefficient tax on top of the time tax.
Have you experimented with the more aggressive initial prompt to avoid the round trip? Something like: "Senior DevOps. Output only the exact AWS CLI command needed to delete an unattached EBS volume. No preamble, no flag explanations, no safety warnings."
I've found this cuts the initial success rate from your 90% to near 100% for straightforward commands, eliminating the second query entirely.
Less spend, more headroom.