# Resume Tailoring - Usage Examples

## Example 1: Internal Role (Same Company)
```
USER: "I want to apply for Principal PM role in 1ES team at Microsoft. Here's the JD: {paste}"

WORKFLOW:
1. Library Build: Finds 29 resumes
2. Research: Microsoft 1ES team, internal culture, role benchmarking
3. Template: Features PM2 Azure Eng Systems role (most relevant)
4. Discovery: Surfaces VS Code extension, Bhavana AI side project
5. Assembly: 92% JD coverage, 75% direct matches
6. Generate: MD + DOCX + Report
7. User approves → Library updated with 6 discovered experiences

RESULT: Highly competitive internal application
```

## Example 2: Career Transition (Different Domain)
```
USER: "I'm a TPM trying to transition to ecology PM role. JD: {paste}"

WORKFLOW:
1. Library Build: Finds existing TPM resumes
2. Research: Ecology sector, sustainability focus, cross-domain transfers
3. Template: Reframes "Technical Program Manager" → "Program Manager, Environmental Systems"
4. Discovery: Surfaces volunteer conservation work, grad research in environmental modeling
5. Assembly: 65% JD coverage - flags gaps in domain-specific knowledge
6. Generate: Resume + gap analysis with cover letter recommendations

RESULT: Bridges technical skills with environmental domain
```

## Example 3: Career Gap Handling
```
USER: "I have a 2-year gap while starting a company. JD: {paste}"

WORKFLOW:
1. Library Build: Finds pre-gap resumes
2. Template: Includes startup as legitimate role
3. Discovery: Surfaces skills developed during startup (fundraising, product dev, team building)
4. Assembly: Frames gap as entrepreneurial experience

RESULT: Gap becomes strength showing initiative and diverse skills
```

## Example 4: Multi-Job Batch (3 Similar Roles)
```
USER: "I want to apply for these 3 TPM roles:
      1. Microsoft 1ES Principal PM
      2. Google Cloud Senior TPM
      3. AWS Container Services Senior PM"

WORKFLOW:
1. Multi-job detection triggered (3 JDs)
2. Library Build once, Gap Analysis deduplicates across all 3
3. Shared Discovery: 30 min session surfaces 5 new experiences
4. Per-Job Processing:
   - Microsoft: 85% coverage, emphasizes Azure/1ES alignment
   - Google: 88% coverage, emphasizes technical depth
   - AWS: 78% coverage, addresses AWS gap in cover letter recs
5. Batch finalization: All 3 reviewed and approved

RESULT: 3 high-quality resumes in 40 min vs 45 min sequential
```

## Example 5: Incremental Batch Addition
```
WEEK 1: Process 3 jobs (Microsoft, Google, AWS) → 40 min
WEEK 2: "Add Stripe and Meta to my batch"
  - Load existing batch with 5 previously discovered experiences
  - Only 3 new gaps (vs 14 original)
  - 10-minute incremental discovery
  - 2 additional resumes in 20 min (vs 30 min from scratch)
```