Principle:Datajuicer Data juicer LLM Content Generation
| Knowledge Sources | |
|---|---|
| Domains | NLP, Data_Generation, LLM |
| Last Updated | 2026-02-14 17:00 GMT |
Overview
A prompt-driven data synthesis technique that uses LLMs to generate question-answer pairs from source text for training data augmentation.
Description
LLM Content Generation uses large language models to automatically create training data from raw source material. Given source text, an LLM is prompted to generate question-answer pairs that capture the knowledge in the text. The generation process uses configurable prompt templates, regex-based output parsing, and retry logic for handling malformed outputs. This enables scaling training data creation beyond manual annotation by leveraging the knowledge synthesis capabilities of LLMs.
Usage
Use this principle when you need to create instruction-tuning datasets from raw text corpora. It is the primary generation step in the LLM Powered Data Generation workflow.
Theoretical Basis
# Abstract algorithm (NOT real implementation)
for text_sample in source_dataset:
# 1. Format prompt with source text
prompt = template.format(text=text_sample)
# 2. Call LLM for generation
response = llm.generate(prompt, temperature=0.7)
# 3. Parse structured output (QA pairs)
qa_pairs = regex_parse(response, output_pattern)
# 4. Add to dataset
for q, a in qa_pairs:
output_dataset.add({'query': q, 'response': a})
The quality of generated data depends on: prompt engineering, model capability, output parsing robustness, and post-generation filtering.