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Prompt engineering guide details structured data extraction from advisories

This tutorial details a method for extracting structured data from unstructured text, specifically focusing on cybersecurity advisories. It outlines a process using the OpenAI API, Pydantic for schema definition and validation, and the `tenacity` library for retry logic. The guide covers system prompt design, few-shot examples, and handling ambiguous fields to reliably parse information like CVE IDs, affected products, and remediation steps into a JSON format. AI

影响 Provides a practical framework for leveraging LLMs in cybersecurity for structured data extraction, improving efficiency and accuracy in analyzing advisories.

排序理由 The article is a technical tutorial explaining a method for using LLMs and specific libraries for data extraction, akin to a research paper or technical guide. [lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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  1. dev.to — LLM tag TIER_1 English(EN) · Ayi NEDJIMI ·

    A practical guide to prompt engineering for structured data extraction

    <p>Extracting structured data from unstructured text is one of the most practical uses of language models in production. Advisory feeds, incident reports, job postings, legal documents — they all contain structured information buried in natural language. Getting that information …