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AI Few-Shot Prompt & Dataset Builder

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Build structured few-shot examples for Claude XML and OpenAI JSONL.

Few-Shot In-Context Learning Generator

Presets:
System Task & Objective Instructions
Demonstration Example Pairs (2)
Example Pair #1
User Input Prompt
Ideal Model Response
Example Pair #2
User Input Prompt
Ideal Model Response
<system_instructions>
Extract customer sentiment, company entity, and product feedback categories from raw reviews into structured JSON.
</system_instructions>

<few_shot_examples>
<example index="1">
  <input>
Acme Corp resolved my billing issue in 5 minutes! Super happy with their support.
  </input>
  <ideal_output>
{
  "company": "Acme Corp",
  "sentiment": "positive",
  "categories": ["billing", "customer_support"]
}
  </ideal_output>
</example>

<example index="2">
  <input>
Beta logistics delayed my delivery by 3 days without any tracking notification.
  </input>
  <ideal_output>
{
  "company": "Beta Logistics",
  "sentiment": "negative",
  "categories": ["shipping", "tracking"]
}
  </ideal_output>
</example>
</few_shot_examples>
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All computation executes exclusively inside your device memory using Web APIs and WebAssembly. No data or files ever leave your browser.

Real-Time Native Execution

Zero network roundtrips. Operates offline without sending external API requests, delivering instantaneous sub-millisecond computation.

Developer-First Architecture

Engineered with RFC standards, cryptographic primitives (Web Crypto Subtle API), and production-grade formatting libraries.

Step-by-Step: How to Use AI Few-Shot Prompt & Dataset Builder

  1. Define Task: Write your overall system instruction or task description.
  2. Add Examples: Create input prompt and expected model output example pairs.
  3. Select Format: Choose Claude XML, OpenAI Chat Messages, or Fine-Tuning JSONL.

Frequently Asked Questions

Why use few-shot prompting?

Few-shot demonstrations significantly improve LLM output accuracy and adherence to complex JSON or formatting constraints without model training.