EXAMPLES
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Click "ADD EXAMPLE" to start building your few-shot blockPreview will appear here
Add at least one input/output pair to generate your few-shot block// build few-shot prompt blocks in seconds
Add input/output pairs to build few-shot prompt blocks instantly. Preview the formatted result ready to paste into any LLM prompt or API call.
No examples yet
Click "ADD EXAMPLE" to start building your few-shot blockPreview will appear here
Add at least one input/output pair to generate your few-shot blockCustomize your input/output field names (e.g. "User", "Assistant" or "Question", "Answer").
Click "Add Example" and fill in each input/output pair. Add as many as you need.
Select XML tags for Claude, Markdown for general use, JSON for APIs, or ChatGPT role messages.
Click "Generate" or watch the live preview update. Copy and paste into your system prompt or API call.
Few-shot prompting is a technique where you provide an LLM with a small number of input/output examples before asking it to perform a task. This builder lets you compose those example blocks in the exact format your chosen model expects — whether that's XML tags for Claude, role messages for ChatGPT, or clean JSON for the API.
Few-shot prompting is a technique where you give a language model a handful of input/output examples before asking it to perform a new task. The model learns the pattern from your examples and applies it to new inputs — without any fine-tuning required.
Typically 2–10 examples work well. Too few and the model may not grasp the pattern; too many and you waste token budget. Aim for diversity — cover edge cases, different lengths, and varying styles within your examples.
Use XML Tags for Claude (Anthropic recommends this). Use ChatGPT Role Messages for the OpenAI API. Use Markdown for general docs or human-readable prompts. Use JSON when building datasets or calling APIs programmatically.
Yes — use the ↑ ↓ arrow buttons on each pair to move them up or down. Research shows that the order of few-shot examples can affect model output, so experiment with different orderings.
The token estimate shown is a rough approximation (chars / 4) for the output block only. Your actual token usage will include your system prompt, user message, and model response. Use this as a rough guide when budget is tight.
Yes — the JSON format option outputs a structured JSON array of your pairs, which you can save and reload. The plain text, XML, and Markdown formats are all copy-paste ready for direct use in your prompts.
Zero-shot means giving the model a task with no examples — just instructions. Few-shot means providing 1 or more examples before the task. One-shot (a single example) falls in between. Few-shot typically produces more consistent, format-accurate output.
No — everything runs entirely in your browser. Your examples are never uploaded to a server, stored in a database, or sent anywhere. The tool is 100% client-side JavaScript.
A few-shot example builder is a prompt engineering tool that helps you structure input/output pairs into the exact format that large language models (LLMs) expect. Instead of manually formatting your examples each time you write a new prompt, this tool lets you add pairs interactively, choose a format, and copy a clean, ready-to-use prompt block in seconds.
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Large language models are trained on enormous corpora of text that include countless patterns of input and response. When you provide examples in your prompt, you're essentially activating the model's in-context learning ability — guiding it to match the style, format, and logic of your demonstrations without changing any model weights.
Research consistently shows that few-shot prompting outperforms zero-shot (no examples) on tasks involving specific formatting, classification, extraction, translation, and structured generation. Even 2–3 well-chosen examples can dramatically improve output consistency.
This tool supports five output formats to match your LLM workflow:
<example>, <input>, and <output> tags — the format recommended in Anthropic's prompt engineering documentation for Claude models.user and assistant role message objects compatible with the OpenAI Chat Completions API.The quality of your examples matters more than the quantity. Here are key principles to keep in mind:
Every example you add costs tokens from the model's context window. A rough rule of thumb is 1 token ≈ 4 characters for English text. The tool displays an estimated token count for your output block so you can stay within budget. For most modern models (GPT-4o, Claude 3.5 Sonnet), context windows are large enough for 10–20 detailed examples without issue. For smaller or older models, keep your examples concise.
A common question is when to use few-shot prompting vs. fine-tuning. Few-shot prompting is fast, flexible, and requires no GPU or training data pipeline — just write your examples and go. Fine-tuning requires a substantial labeled dataset, compute time, and ongoing maintenance. For most prompt engineering use cases, few-shot prompting is the right starting point. Reserve fine-tuning for high-volume production tasks where consistency and latency are critical and you've already exhausted what good prompting can achieve.
The most effective workflow is to use this tool iteratively. Start with 2 examples, test the prompt with your model, observe where it fails, add a targeted example that corrects that failure, and repeat. This incremental approach — sometimes called prompt debugging — typically converges to a solid few-shot block in 3–5 iterations without burning through large amounts of token budget in testing.
Export your finalized example block in JSON format so you can load it back in later sessions and continue refining without starting from scratch. The JSON format is also useful for sharing prompt templates with teammates or storing in a prompt library.