Judge your Resume with AI. What’s the worst that can happen?

TABLE OF CONTENTS

Summary

Bear’s take

What you’ll learn

Key steps

Next step

Links or resource

Judge your Resume with AI. What’s the worst that can happen?

How to use LLM to get honest feedback on your resume and improve your job application skills

AI for career

Bear Liu

Jun 28, 2024

How to use LLM to get honest feedback on your resume and improve your job application skills

Use Case: Using AI to analyze and critique resumes for self-improvement and job readiness.

Tool: txtai for text extraction and the Mistral-7B-OpenOrca-AWQ model for AI analysis.

Time for Learning: 1-2 hours to understand and implement the basic use case.

Read the original article:

Judge your Resume with AI

What’s the worst that can happen?

https://medium.com/neuml/judge-your-resume-with-ai-4223a2803509

Summary

This guide from David Mezzetti explores how AI can be used to analyze and critique resumes using retrieval augmented generation (RAG) with txtai. It walks through the setup of necessary libraries, the extraction of text from a resume, and the application of a large language model (LLM) to provide feedback. By changing the AI's persona from friendly to critical, users can gain varied insights into their resumes. This hands-on approach helps users understand the potential of AI in real-life applications like job searching.

Bear’s take

This tutorial is a fascinating example of how AI can be leveraged for practical and personal uses, like resume evaluation. The dual approach of using both friendly and critical AI personas provides a well-rounded perspective on one’s strengths and weaknesses. It’s particularly intriguing how AI can be used to give brutally honest feedback that might be uncomfortable but necessary for growth. For instance, the AI suggesting potential weaknesses like time management or delegation offers valuable insights that one might overlook. This use of AI not only makes resume improvement more accessible but also emphasizes the importance of self-awareness in career development.

What you’ll learn

In this tutorial, you will learn how to use AI to analyze and critique your resume, leveraging the power of retrieval augmented generation (RAG) and large language models (LLMs). First, you'll set up the necessary libraries, such as txtai and autoawq, to handle text extraction and model operations. By exporting your resume from LinkedIn as a PDF, you can use txtai's text extraction capabilities to convert the document into plain text.

Once the text is extracted, you will load a quantized LLM, specifically the Mistral-7B-OpenOrca-AWQ model, to analyze the resume. The tutorial includes a method to query the LLM with specific questions about the resume, providing detailed feedback. Initially, the AI is set to a friendly assistant persona, offering constructive and encouraging insights. For example, it can summarize your career, highlight your strengths, and even compare you to famous personalities based on your accomplishments.

To provide a balanced perspective, the guide also shows how to change the AI's persona to a harsh critic. This critical mode gives a more brutally honest assessment, pointing out potential weaknesses, such as time management or communication skills. This dual approach helps users gain comprehensive feedback, which can be invaluable for personal and professional growth.

The tutorial demonstrates the flexibility of RAG in tailoring AI responses based on different contexts and prompts. By experimenting with these personas, users can better understand how AI can be used to refine their resumes and prepare for job applications more effectively. The guide concludes with practical insights and encourages users to try the process with their own resumes, making the learning experience both interactive and impactful.

Key steps

Setup Libraries: Install txtai and autoawq using pip.

Export Resume: Download your resume from LinkedIn in PDF format.

Extract Text: Use txtai’s Textractor to convert the PDF into text.

Load LLM: Initialize the Mistral-7B-OpenOrca-AWQ model.

Query AI: Write and run queries to get feedback on your resume.

Change Persona: Modify the system prompt to switch between friendly and critical AI personas.

Analyze Feedback: Review AI responses to understand strengths and areas for improvement.

Next step

Try It Yourself: Export your LinkedIn resume and follow the tutorial steps to get AI feedback.

Experiment with Prompts: Adjust the system prompts to see different perspectives.

Improve Your Resume: Use the feedback to make necessary changes and enhancements.

Links or resource

Tools Mentioned: txtai, Mistral-7B-OpenOrca-AWQ model

Additional Resources:

学习如何利用 AI 获取对你简历的诚实反馈，并提升你的求职技能。

用例：使用 AI 分析和评估简历，以实现自我提升和求职准备。

工具：txtai（用于文本提取）和 Mistral-7B-OpenOrca-AWQ 模型（用于 AI 分析）。

学习时间：理解和实现基本用例需要 1-2 小时。

总结

本指南探讨了如何利用 AI 分析和评估简历，采用检索增强生成 (RAG) 技术和 txtai 工具。教程涵盖了必要库的设置、简历文本的提取，以及应用大语言模型 (LLM) 提供反馈的方法。通过将 AI 的角色从友好助手切换到严格批评者，用户可以获得对简历的多方面见解。这种实操方法帮助用户理解 AI 在实际应用（如求职）中的潜力。

你将学到什么

在本教程中，你将学习如何利用 AI 分析和评估你的简历，运用检索增强生成 (RAG) 和大语言模型 (LLM) 的强大功能。首先，你将设置必要的库，如 txtai 和 autoawq，以处理文本提取和模型操作。通过从 LinkedIn 导出你的简历为 PDF 文件，你可以利用 txtai 的文本提取功能将文件转换为纯文本。

一旦提取了文本，你将加载一个量化的大语言模型，特别是 Mistral-7B-OpenOrca-AWQ 模型，来分析简历。教程包括一种方法，向 LLM 提出有关简历的具体问题，以获取详细反馈。起初，AI 设定为友好助手角色，提供建设性和鼓励性的见解。例如，它可以总结你的职业生涯，突出你的优势，甚至根据你的成就与你比较著名人物。

为了提供平衡的观点，本指南还展示了如何将 AI 的角色切换为严厉的批评者。这种批评模式提供了更为坦率的评估，指出可能的弱点，如时间管理或沟通能力。这种双重方法帮助用户获得全面的反馈，对于个人和职业成长都非常宝贵。

教程展示了 RAG 在不同上下文和提示下定制 AI 响应的灵活性。通过试验这些角色，用户可以更好地理解 AI 如何用于优化简历，并更有效地准备求职。本指南以实用的见解作为结尾，鼓励用户尝试用自己的简历进行此过程，使学习体验既互动又有影响力。

我的收获

这篇教程展示了 AI 如何用于实际和个人用途，如简历评估。这种友好和批评 AI 角色的双重方法提供了对个人优势和弱点的全面看法。特别有趣的是，AI 可以提供直接而诚实的反馈，尽管可能让人不舒服，但对于成长是必要的。例如，AI 指出可能的弱点，如时间管理或任务委派，提供了可能被忽略的宝贵见解。这种 AI 应用不仅使简历改进更加方便，还强调了自我意识在职业发展中的重要性。

关键步骤