Few-Shot Example Formatter
Format input/output example pairs into a clean few-shot prompt block — Labeled, XML, Q&A or Markdown. Free, runs in your browser.
Few-Shot Example Formatter
How to Use the Few-Shot Example Formatter
Add your example pairs
For each example, type the input (what the user sends) and the output (the ideal model reply). Use the "Add example" button to add as many pairs as you need — two or three well-chosen ones usually do the job.
Pick a format
Choose how the pairs are laid out: Labeled (Input:/Output:), XML (<example> tags), Q&A (Q:/A:), or numbered Markdown. Different models and prompts prefer different shapes — switch any time and watch the block rebuild.
Watch it assemble live
The formatted few-shot block builds as you type. Blank pairs are skipped automatically, so you can leave a spare row open without polluting the output. Everything happens in your browser — nothing is uploaded.
Copy into your prompt
Hit Copy and paste the block into your system prompt, user message, or prompt template — right before the real task. Consistent, well-formatted examples are the cheapest way to steer a model's behaviour.
Examples Are the Cheapest Way to Steer a Model
What few-shot prompting actually does
Few-shot prompting means showing a model a handful of worked examples — pairs of input and the desired output — before you ask it to handle the real case. Instead of describing the behaviour you want in prose ("be concise, use this tone, return JSON"), you simply demonstrate it. The model reads the pattern in your examples and continues it. This is a form of in-context learning: the model isn't retrained or fine-tuned, it just picks up the shape, style, and format you've shown and applies it to the next input. For classification, extraction, formatting, rewriting, and tone-matching tasks, two or three good examples often outperform a paragraph of instructions, because a concrete demonstration removes ambiguity that words leave open.
The catch is that examples only help when they're consistent. If one example answers in a full sentence and the next answers in three words, the model can't tell which pattern to follow — and it will improvise. The same goes for formatting: mixed delimiters, inconsistent labels, and ragged spacing all leak into the output. That's exactly the problem this tool solves. You enter clean input/output pairs once, choose a single layout — Labeled, XML, Q&A, or numbered Markdown — and every example comes out in identical, predictable shape. Consistent structure is what makes the model treat your examples as a rule rather than a suggestion.
"Telling a model what you want is a description. Showing it two clean examples is a specification. Specifications win."
Choosing a format and a good example set
Which layout you pick matters more than people expect. Labeled (Input: / Output:) is the plain, universal default that works everywhere. XML tags such as <example> are especially clean for models like Claude that parse structured delimiters well, and they keep multi-line inputs from bleeding into each other. Q&A is natural for question-answering and chat-style tasks. Markdown with numbered headings reads well to humans and survives copy-paste into docs and notebooks. Beyond format, curate the set: cover the edge cases you care about, include at least one tricky or ambiguous example, and order them so the hardest case isn't first. Keep the examples short — they consume context on every call, so three sharp pairs beat ten sprawling ones. Then place the block right before your actual request, and the model has a clear, unambiguous pattern to follow. Fill the rows, pick a format, copy, and paste — it all runs in your browser and never leaves your machine.
10 Facts About Few-Shot Prompting
Few-shot means showing examples; zero-shot means asking with none. Examples often beat instructions.
It works through in-context learning — the model isn't retrained, it just continues your pattern.
Consistency is everything: mismatched example styles confuse the model more than no examples at all.
Two or three sharp examples usually outperform ten sprawling ones — examples eat context every call.
XML delimiters keep multi-line inputs from bleeding together — handy for models that parse tags well.
Including one edge case in your examples teaches the model how to handle the tricky ones.
Example order can matter — avoid putting the hardest or weirdest case first.
Few-shot shines for classification, extraction and formatting where the output shape is fixed.
Place the example block right before the real task so the pattern is fresh in context.
This formatter runs entirely in your browser — your examples are never uploaded.
Frequently Asked Questions
- Few-shot prompting means showing a model a few worked examples — pairs of input and the desired output — before asking it to handle a new case. The model reads the pattern and continues it, which is often more reliable than describing the behaviour in words.
- Labeled is the universal default and works everywhere. XML tags are clean for models like Claude that parse delimiters well and keep multi-line inputs separate. Q&A suits chat and question-answering tasks. Markdown reads nicely to humans and pastes well into docs. Switch formats any time and the block rebuilds.
- No. The block is assembled entirely in your browser with plain JavaScript. Nothing you type is sent to any model, server, or third party, and nothing is saved.
- Usually two to five. A handful of sharp, consistent examples beats a long list — every example consumes context on each call. Cover the cases you care about, including one tricky edge case, then stop.
- They're skipped. A pair only appears in the output once both fields — or at least one — contain text; rows where both input and output are empty are ignored, so you can leave a spare row open without affecting the result.
- Put it in your prompt right before the real task — in a system prompt, a user message, or a prompt template. The output is plain text, so it works with ChatGPT, Claude, Gemini, and most agent frameworks.
- Often, yes — for classification, extraction, formatting and tone-matching tasks, a concrete demonstration removes ambiguity that prose leaves open. The strongest prompts usually combine a short instruction with a few clean examples.
- It can. Models are somewhat sensitive to ordering, so avoid leading with the hardest or most unusual case. A reasonable rule is to start with a clear, typical example and place edge cases later in the set.
- Yes. The formatted block is plain text you can drop into an agent's system message or any prompt template. Demonstrating the exact input/output shape you expect is a reliable way to constrain an agent's behaviour.
- Completely free, with no account or sign-up, and no limit on use. It runs in your browser and collects no data.
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