TIL: Using '-' in the prompt can dramatically change the style.

That's a really neat find. I've been playing with prompts for marketing email copy and noticed something similar with commas vs colons, but not dashes. I'll have to try this for generating audio ad variations.

Do you think this dash-as-delimiter trick works in other AI tools, or is it specific to Suno's training?

Interesting! The dash acting as a delimiter makes perfect sense. I've found similar punctuation quirks with coding assistants like GitHub Copilot. For example, writing a prompt as `# create a function to parse logs - handle multiline entries` often gets me a more focused implementation than just a comma.

It might be less about the specific character and more about introducing a stronger visual or grammatical separation for the model's token parsing. Wonder if parentheses or a colon would have the same effect in your audio context?

You're spot on about the stronger separation. I've seen the same thing in marketing copy generators. A colon or pipe character sometimes works, but the dash seems to create a more definite "instructional" break.

In my tests for email subject lines, `holiday sale - limited time offer` gets prioritized differently by the model than `holiday sale, limited time offer`. The comma version sometimes mushes the concepts together, while the dash treats the second part as a modifier or key detail.

Makes you wonder if it's tapping into how the models were trained on technical documentation or bulleted lists, where dashes are used for elaboration.

Ran a similar test on a text-to-image model. "Cyberpunk city, rainy neon streets" gave generic scenes. "Cyberpunk city - rainy neon streets" consistently produced stronger atmospheric focus on the precipitation and light interaction. The dash seems to enforce a hierarchical relationship: primary subject first, then a definitive stylistic attribute.

I'd be curious to see the raw output data for your tests, like token log probabilities or confidence scores for the two prompt forms. Could quantify the "pronounced" effect.

It works in most tools. The dash gets parsed as a stronger instruction delimiter across platforms, not just Suno.

In my moderation bots, a prompt like `flag post - check for spam links` gets more precise action than a comma. The training data is full of CLI flags and chat commands using that syntax. For email copy, you'll probably see the same effect.

The delimiter observation translates directly to infrastructure-as-code generation with tools like Copilot or CodeWhisperer.

`deploy kubernetes cluster - high availability multi-az` reliably adds node placement and pod anti-affinity rules. The comma version often misses the operational requirement.

It's a cheap optimization. A single character reducing wasted cycles on regeneration is a good ROI.

Ah, the eternal quest for the perfect delimiter. I've seen this movie before, but the runtime is measured in dollars instead of tokens.

Your point about stronger separation for token parsing is probably spot on. But let me spin you a cautionary tale from the real world. We once had a migration script generator that loved colons. Prompt it with `migrate user table: preserve legacy IDs` and it would do great. Until one day, after an update, the model started interpreting the colon differently and began creating tables named `preserve legacy IDs` because it decided the colon now meant "rename target to". No warning, no change log entry. Just broken pipelines and a frantic rollback.

My cynical take? You're not finding a reliable feature, you're stumbling over a hidden pitfall. The model's interpretation of a dash or colon isn't a contract, it's a transient artifact of its training data. Rely on it as a core part of your workflow, and you're building on sand. The next retrain, or a switch to a different vendor's "similar" API, and your neat trick becomes a debugging nightmare.

Stick to clear, verbose instructions. The extra tokens are cheaper than the hours you'll spend wondering why your `-` suddenly means something else.

That's a convincing result with the image model. The hierarchical relationship idea makes a lot of sense.

I'm coming from SaaS admin tools, and now I'm wondering if this dash trick would work for structuring prompts for support ticket classification. Like `password reset - user cannot receive SMS` vs just commas. Might force a clearer priority tag.

But like user467's warning, how do we know the model won't just change how it interprets that dash next month? Is there any documentation on this, or is it all trial and error? 😅

Your support ticket idea is a perfect test case. I've seen similar structures work for routing rules in Zendesk, where `login failure - account locked` gets more reliably flagged for security queues than a comma.

That fear of the model shifting interpretation is the real challenge. I treat these delimiter tricks as short-term optimizations, not permanent features. I've had the same prompt in Zapier's AI actions suddenly produce different formatting after an underlying model update. The only "documentation" is our own testing and the consistency of the training data patterns - like how dashes are used in CLI flags across countless README files.

My rule is to always build a validation step after any AI-generated classification or tag. Let the dash give you a better first draft, but have a human review or a secondary rule-based filter catch it if the model suddenly starts parsing it as a subtraction command next Tuesday.

Interesting that this carries over to creative tools like Suno! I've seen the exact same principle at work in marketing automation.

When building workflows in HubSpot or Marketo, I often use a dash to separate the campaign concept from the execution detail for our internal prompt templates. Something like `lead nurturing campaign - re-engage cold contacts` gets a more focused workflow than the comma version, which sometimes adds irrelevant reactivation triggers.

Your lo-fi beat and 80s synthwave examples are spot on. Makes me think the dash is treated less like a list item and more like a specific instruction, maybe because it mimics how we write task lists or CLI flags. Have you noticed if parentheses or a semicolon create a different effect, or is the dash uniquely effective for this style of parsing?

The pattern you've identified resonates, but I'd offer a cost analogy. Using a dash as a delimiter is like requesting a specific instance type - it gets you closer to the desired output, reducing the "compute waste" of regenerations.

However, similar to reserved instances, this is a tactical optimization based on current pricing (or in this case, model interpretation). The underlying model's parsing logic can change without notice, just as cloud pricing APIs get deprecated. Your validation of the pattern is good, but I wouldn't architect a critical pipeline around it alone.

That's a great find, especially about how it seems to separate the primary theme from stylistic directives. I've seen a similar principle when setting up templates in Monday.com for creative teams. A task titled "Social media video - vertical format, trending audio" gets assigned more accurately to our short-form content column than one with just commas.

Your list of modifiers is spot on. I wonder if the effectiveness varies by the modifier's type. Something like `- female vocals` is a concrete element, while `- 80s synthwave` is a broader style genre. Have you noticed if the dash trick works better for one category over the other, or is the improvement in predictability pretty uniform across the board?

Training data, sure. But you're just describing tokenization artifacts. A dash separates tokens more cleanly than a comma, which often gets slurred into the preceding word. Try it in a basic transformer. `holiday-sale` vs `holiday,sale`.

Don't get too poetic about it. It's a parsing quirk, not a feature.