Persuasive Resumes

If you find yourself writing a resume after years of employment right now, first, I’m sorry to hear it. My heart is with you. Second, you can do this. Don’t go alone. Find friends to speak with about your progress, and remember that you are the authority over your own value. If employers reject you, that means you weren’t what they wanted, not that you aren’t wantable. Keep going, keep improving.

Your writing style in a resume may matter more than the experience listed on it. Your resume and cover letter are your chance to persuade the company to interview you, so write persuasively! The “Amazon writing style” can help you avoid empty vagaries like “I launched a successful product with lots of users” and embrace specifics like “My tiny team of three people launched Y in only Z months, and had NNN users within the first 4 weeks, all with a crash-free rate of 99.87%”.

This could be a great use of AI and language models, not to write your resume for you, but to offer you critique, and help you make better and better iterations of your resume. Start with something, get feedback from both humans and AI, make changes, repeat.

This blog post from Armand Patella has formatted points from the Amazon writing style into a YAML dict that LLMs should be pretty good at understanding:

https://www.armandpatella.com/amazon-writing-tips

I’ll copy-paste it here in a code block in case that post becomes unavailable:

writing_guidelines:
  version: "2024.1"
  source: "Write Like an Amazonian"
  last_updated: "2018-11"
  
  core_principles:
    - name: "Direct Communication"
      rules:
        sentence_structure:
          max_words: 30
          response_types: ["Yes", "No", "Number", "I don't know (with follow-up)"]
        word_replacements:
          - replace: "due to the fact that"
            with: "because"
          - replace: "totally lack the ability to"
            with: "could not"
        
    - name: "Data-Driven Writing"
      rules:
        metrics:
          - replace: "subjective statements"
            with: "quantifiable metrics"
          examples:
            - before: "performance is much faster"
              after: "reduced TP90 latency from 10ms to 1ms"
            - before: "nearly all customers"
              after: "87% of Prime members"
            
    - name: "Technical Clarity"
      rules:
        acronyms:
          format: "{full_term} ({acronym})"
          example: "Non-Disclosure Agreement (NDA)"
        prohibited_phrases:
          - "would help the solution"
          - "might bring clarity"
          - "should result in benefits"
          - "significantly better"
          - "arguably the best"

  validation:
    required_elements:
      - quantifiable_metrics
      - specific_data_points
      - defined_acronyms
    prohibited_elements:
      - weasel_words
      - subjective_adjectives
      - undefined_acronyms

If you try it, let me know

I haven’t tried using the above prompt with an LLM to review a resume. If you try it, please leave a comment and let me know how it works for you. If you find a better prompt, please let me know so I can update this post 🙏