Job description tailoring #

For each job, Raven extracts keywords from the title and matches your resume bullets to produce tailored application content.

Output columns #

How matching works #

1. Keyword extraction — splits title on common separators, filters stop words
2. Bullet scoring — each resume bullet scored by keyword overlap with title
3. Action words — role-type heuristics (backend → “architected”, “scaled”; ML → “trained”, “deployed”)
4. Top N — resume.max_bullets from profile (default 5)

Example #

Job title: Senior Backend Engineer — Node.js

Generated fields:

jd_keywords: senior, backend, engineer, node
action_words: architected, scaled, optimized, shipped
tailored_bullets:
  - Built REST APIs in Node.js serving 10M requests/day
  - Migrated monolith to microservices, reducing latency 40%

Email integration #

Tailored bullets populate {{highlights}} in your outreach template:

outreach:
  body: |
    {{greeting}},

    Relevant experience for the {{title}} role:

    {{highlights}}

    Best,
    {{senderName}}

Form applications #

For ATS form jobs, tailored_bullets appear in the form_steps guide — copy/paste into “Why are you interested?” or “Relevant experience” fields.

Improving match quality #

1. Write resume bullets with concrete tech stacks and metrics
2. Mirror vocabulary you target (e.g. “platform engineer”, “LLM”, “Kubernetes”)
3. Keep bullets under ## Experience — parser ignores other sections for matching