Mastering One-Liner Prompts

Introduction

The funny thing about prompt engineering is that everyone tells you to “just write clear instructions” — but then you hit a wall the moment you can’t use newlines. Most examples you see online look neat and nicely formatted. Reality? Sometimes you don’t get that luxury. You’re inside a shell command, a URL, or some scripting glue where the model only gets one line.

This post is about surviving in that environment and bring some interesting topics like how LLM understand punctuations and how can we take advantage of this. I’ll share the way I think about it, some tricks I use, and why sometimes you don’t even need to fight too hard. Think of it as the dirty hacks that make your one-liner prompts still readable and still effective.

Why Newlines Matter

Absolutely, they’re crucial. Think of newlines as the breath between thoughts—they help organize ideas and keep things clear for both people and technology. When newlines aren’t an option, you just have to get a bit inventive.

Here’s how to keep things flowing:

When handling separate tasks (like “Summarize” followed by “Critique”)

If the tasks are brief and closely related, a semicolon or comma does the trick. It breaks down the steps visually, making it easier to follow along for everyone involved.

For complex lists or nested information

When commas are already in use, it can get messy. Instead, try out brackets [], pipes |, or arrows -> to keep everything sorted.

For interactive conversations

This approach mimics real chat and helps systems refresh context with each line. Combine the chat into a single sentence using clear labels like User: and AI:.

So, while newlines are definitely handy, you can skip them if you’re ready to think outside the box.

Prompt Rules

Those are generic but important tips for writing prompts, define the structure and clarify corner cases. Let’s review them because we will need them for later

• Start with Action Verbs: Importance of initiating prompts with clear, directing actions.(e.g., Summarize, Compare, Explain).
• Limit Contextual Complexity: Keeping the main concept central with minimal keyword anchoring. 1-2 key words.
• Avoid Excessive Nesting: How simplicity enhances the model’s understanding and reduces confusion.
• Provide Examples: (Use the e.g. keyword)
• Conclude with Clear Instructions: Ensuring the model understands the expected output format.

The one-liner mindset

The trick is to stop thinking of your prompt as a paragraph and instead see it as data. You’re encoding instructions in a single line, so punctuation becomes your structuring tool. That means:

• Use ; as the new newline. Some models permit the use of the raw \n, but it’s not always portable.
• Use (), [] or {} for grouping concepts. Use quotes " or apostrophes ' to group words.
• Use -> arrows to define sequences, This can be a replacement for numbered lists.
• Use | as a visual separator (like a fake table). The / also works to separate elements when options are involved (Yes/No)
• Use : to introduce contents for a topic (kind of key: value).
• Use = to create equivalences, aliases for later substitutions in order to compact text.

For Example. Instead of:

Summarize the text below.
Then give me 3 questions.
Answer them briefly.

Do:

Summarize the text below; then give me 3 questions; answer them briefly.

Practical hacks

Here are some tricks I end up using all the time:

Role markers – If you need a back-and-forth style:

User: What’s 2+2? AI: 4. User: Now explain why.

Fake bullets – - or * don’t really need a newline. Example:

Tell me: - summary of the text - its sentiment - one critique

Escape the chaos with JSON – If your environment lets you, wrap the whole instruction in JSON. Models love it:

{"task": "summarize", "then": "critique", "extra": "questions"}

Sorting a numbered list that may work with 200MB models:

Sort this numbered list: ((3) last, (2) first, (4) jeje, (1) win)

Notice the following details:

• Lispy syntax
• Prompt begins with a verb / action.
• Introduce the type of data (numbered list)
• Use colon : to separate action and data
• Balanced parenthesis (1) ..
• List contained between parenthesis
• Using , comma to separate elements

Attachments

When you need to include the contents of a file or reference some context data inside the same prompt, using XML -like tags is usually a good idea, but it’s not the only option. Some of you may ask if base64 encoding works, well that’s kind of tricky and usually depends on some agentic helpers.

Here there are some rules to help you on that:

• Use XML words like <CONTEXT>...</CONTEXT>
• Tricky [BEGIN] and [END] words also work well
• The 3 backticks like in markdown, using spaces instead of newlines
• Use real XML with <![CDATA[ ... ]]>
• Some models use <|...|> to differentiate from XML and reduce collisions with inlined text

With all the rules above always ensure the open/close tags are not used inside inlined text

Final hints

At some point you’ll ask yourself: “Why am I even trying so hard?” The answer: because sometimes you don’t control the interface. CLI tools, HTTP query strings, or environments that choke on literal newlines force you into this game.

A few fallback patterns:

• Comma and semicolon stacking – works in 90% of cases.
• Explicit numbering – e.g., (1) (2) (3) inline. Note that unbalanced parenthesis like 1) is usually a bad idea because it can cause bound problems. An alternative can be to use #1 using the #
• Arrows – surprisingly effective for chaining: step1 -> step2 -> step3.
• UPPERCASE and bold - keywords can be surrounded by two asterisks ** or use uppercase letters to make them more important.
• Ellipsis - using the ... at the end of enumerations may help the model understand the given input is incomplete.

It’s not elegant, but neither is cramming everything in one line. The point is: don’t break your flow just because the environment won’t accept \n.

Conclusion

Prompt engineering isn’t about being fancy — it’s about being clear under constraints. One-liners are just another constraint. If you treat punctuation as your layout system, you can still keep structure, flow, and clarity.

And honestly, once you practice this a bit, you’ll find that models don’t actually need as much whitespace as humans do. They just need you to be explicit.

So next time you hit an environment that laughs at your newlines? Don’t panic. Just go semicolon mode!

PD: This whole post could be written in a single oneliner.

–pancake