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- Conclude with quantified achievement demonstrating relevant technical proficiency
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- **REFLECT COMPANY CHARACTER:** Adapt tone to `tone_and_priorities` (e.g., dynamic language for "fast-paced," metrics for "data-driven," teamwork for "collaborative")
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- **SUBTLE CULTURAL ALIGNMENT:** If authentic, incorporate 1-2 concepts from `culture_and_values` (e.g., mention innovation if valued)
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- Write as cohesive, conversational paragraph (NOT bullet points, NO company names)
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Example: "Software Engineer with 8+ years building scalable cloud infrastructure on AWS. Led development of microservices handling 50M+ daily requests, reducing latency by 40%. Passionate about rapid iteration and data-driven optimization."
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**STEP 3 - TAILOR WORK EXPERIENCE:**
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**Role Title Handling (CRITICAL AUTHENTICITY RULE):**
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- If most recent title is functionally similar but uses different terminology (e.g., "Software Developer" vs. "Software Engineer"), align it to target title
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- NEVER change seniority level (e.g., "Engineer" → "Senior Engineer") or core function (e.g., "Data Analyst" → "Data Scientist")
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- Keep ALL earlier titles unchanged
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- If in doubt, keep original title and use bullets to emphasize relevant responsibilities
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**Bullet Point Crafting:**
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- Select 5 most relevant accomplishments per role from master resume
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- Rewrite using STAR method: Action + Quantifiable Result/Impact
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- Keep to 1-2 lines (15-30 words) for scannability, but prioritize clarity—allow slight extensions for complex achievements
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- **DEMONSTRATE SOFT & MANAGEMENT SKILLS** through concrete examples:
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* For collaboration: "Partnered with design and product teams of 12 to..."
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* For mentorship: "Mentored 3 junior engineers, improving team code quality by 25%"
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* For stakeholder management: "Presented technical roadmap to executive leadership"
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- Use strong, varied action verbs: Technical (Architected, Built, Optimized); Leadership (Led, Managed, Mentored); Research (Analyzed, Investigated)
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- Use "leveraged" or "spearheaded" ONLY sparingly and tied to specific measurable outcomes
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- Vary sentence structures (action-first, result-first, time-based) for natural flow
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- Include 1-2 metrics per bullet where measurable; emphasize scope/complexity if not quantifiable—don't force metrics
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- Format metrics consistently (%, K, M)
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- Integrate keywords from `skills.technical` and repeated phrases from `responsibilities_and_qualifications` naturally where they authentically fit
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**STEP 4 - CURATE SKILLS:**
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- Filter master resume skills to match `skills.technical` from JobAnalysis—remove unaligned items
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- Organize into clear categories: Programming Languages, Frameworks, Databases, Cloud Platforms, Developer Tools, Methodologies
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- List ONLY technical/hard skills (soft/management skills are demonstrated through experience bullets, NOT listed here)
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- Use exact industry-standard names (JavaScript not Javascript, MySQL not Mysql)
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- Avoid redundancy (don't list both "Python" and "Python 3.x")
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- **BONUS SKILLS STRATEGY:** Review `skills.bonus` from JobAnalysis—if possessed, include these differentiators prominently in relevant categories
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- **IMPLIED SKILLS RULE:** Add skills not in master resume ONLY if directly implied by existing projects/work
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* ACCEPTABLE: Add "Python" if project used "Django"
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* NOT ACCEPTABLE: Don't infer "Distributed Systems" from basic "AWS" usage
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**STEP 5 - REFINE PROJECTS:**
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- Write 2-4 sentence narratives using problem-solution-result structure
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- Highlight technologies matching `skills.technical` from JobAnalysis
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- Include measurable outcomes where available
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- **USE PROJECTS TO SHOWCASE BONUS QUALIFICATIONS:** If `skills.bonus` contains technologies/skills not covered in work experience, feature them here
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- Use projects to fill competency gaps not fully demonstrated in work experience
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- Write conversationally with natural flow
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**STEP 6 - UPDATE EDUCATION:**
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- List up to 5 relevant courses per degree directly relating to `skills.technical` or `responsibilities_and_qualifications`
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- Use natural phrasing: "Relevant coursework includes..."
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- Omit unrelated coursework
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**STEP 7 - FINAL VALIDATION:**
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Verify the resume fully aligns with ALL aspects of the JobAnalysis report:
- Confirm master resume has: work experience (titles/dates/bullets), skills, education
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- Calculate skill overlap: If <30% match between master resume skills and JobAnalysis technical skills, STOP and output:
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{"error": "fundamental_mismatch", "overlap_percentage": X, "message": "Insufficient alignment between candidate background and role requirements"}
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**CORE PRINCIPLES (Apply to ALL steps):**
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1. **AUTHENTICITY** - Never fabricate skills/experience not in master resume. Never change seniority level (Junior→Senior) or core function (Analyst→Scientist).
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2. **KEYWORD OPTIMIZATION** - Target 50-70% coverage from JobAnalysis. Prioritize keyword placement: Summary > Recent Role > Projects > Earlier Roles. Max 3 uses per keyword.
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3. **NATURAL LANGUAGE** - Vary sentence structures. Avoid AI patterns: "leveraged/spearheaded" (unless tied to specific metrics), "synergy", robotic repetition.
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4. **DEMONSTRATION** - Show soft/management skills through examples, NEVER list them. Technical skills only in Skills section.
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5. **RECENCY** - Focus 70% content on last 5 years of experience. Condense older roles to 2-3 bullets max.
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**STEP 1 - KEYWORD ANALYSIS & MAPPING:**
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Extract from JobAnalysis: `skills.technical` (Skills section), `skills.soft` (demonstrate in bullets), `skills.management` (show through leadership examples),
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`skills.bonus` (differentiators if possessed), `responsibilities_and_qualifications` (repeated phrases = highest priority). Map authentic master resume experiences to requirements.
Adapt tone to JobAnalysis: Fast-paced → dynamic verbs; Data-driven → metrics-heavy; Collaborative → team language; Innovative → new solutions.
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Example: "Software Engineer with 8+ years building scalable AWS infrastructure. Led microservices handling 50M+ daily requests, reducing latency 40%."
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**STEP 3 - WORK EXPERIENCE:**
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**Title Handling:** Align most recent title ONLY if functionally identical (Developer<-->Engineer OK, Analyst<-->Scientist NOT OK). When unclear, keep original and emphasize relevant duties in bullets.
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**Bullets (5-6 for recent roles, 2-3 for older roles per RECENCY principle):**
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- **Review ALL master resume bullets** for each role. Select most relevant content—combine related achievements to preserve impactful details without losing key information.
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- STAR format: Action + Quantifiable Result (15-30 words, max 35 for complex achievements)
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- **Include metrics where authentic** (1-2 per bullet max). Use %, K, M format consistently. No metrics? Emphasize scope, complexity, or technical depth instead.
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- Examples: Collaboration = "Partnered with X team of N..."; Mentorship = "Mentored N engineers, improving Y by Z%"; Leadership = "Led initiative resulting in..."
- Integrate keywords from `skills.technical` and repeated phrases from `responsibilities_and_qualifications` naturally where authentic fit exists
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**STEP 4 - SKILLS:**
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Filter master skills to match `skills.technical`. Organize: Programming Languages | Frameworks | Databases | Cloud Platforms | Developer Tools | Methodologies.
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Use exact names (JavaScript not Javascript). Include `skills.bonus` if possessed.
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**Implied Skills Rule:** Add ONLY if technological prerequisite (Django→Python OK; AWS→Distributed Systems NOT OK).
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**STEP 5 - PROJECTS:**
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2-4 sentences: Problem → Solution → Result. Feature `skills.bonus` technologies not covered in work experience. Include metrics if available.
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**STEP 6 - EDUCATION:**
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List up to 5 courses matching `skills.technical` or `responsibilities_and_qualifications`. Use natural phrasing: "Relevant coursework includes..."
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**STEP 7 - SELF-CHECK:**
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Verify adherence to CORE PRINCIPLES and keyword distribution targets. Confirm: no pronouns (I/me/we), consistent tense (past for previous/present for current).
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**Master Resume:**
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```
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{master_resume}
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```
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expected_output: >
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A single, clean JSON object that strictly adheres to the `ResumeContent` Pydantic model.
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Do not include any introductory text, explanations, or any content outside of the final JSON object.
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Single JSON object adhering to `ResumeContent` Pydantic model. NO preamble, explanations, or markdown formatting—JSON only.
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