Synthesize structured career components (what_i_did, my_thoughts, performance files) into a cohesive professional resume. Use when generating resumes from extracted yearly data.
Create a polished, professional resume by intelligently combining structured career data from multiple years into a coherent narrative.
Generate RESUME.md by synthesizing:
what_i_did_*.md files (all years)my_thoughts_*.md files (all years)performance_*.md files (all years)basic_info.md (static info: name, contact, education, military, certs)what_i_did_*.md, my_thoughts_*.md, performance_*.md filesbasic_info.mdFor each year's data:
Synthesize from all years:
Example:
Senior Software Engineer with 6+ years building scalable AI/ML systems. Led backend migrations improving query performance by 40%, deployed real-time streaming systems processing 10K+ events/sec, and architected cloud infrastructure serving 100K+ users. Deep expertise in Python, Go, distributed systems, and MLOps, with proven ability to translate complex technical challenges into business value.
Aggregate from all what_i_did_*.md files:
Group logically, prioritize by recency and proficiency.
Synthesize from all three file types:
Example:
## Work Experience
### Senior Software Engineer | Current Company | 2020 - Present
**2024**
- Led backend migration from PostgreSQL to MongoDB, reducing query latency by 40% and improving system scalability
- Architected real-time streaming mosaic processing system handling 10K+ concurrent CCTV streams
- Mentored 3 junior engineers on distributed systems design patterns learned through production challenges
**2023**
- Designed and deployed Naver Cloud tagging system processing 100K+ resources with 99.9% uptime
- Reduced infrastructure costs by 25% through automated resource optimization and monitoring
- Developed expertise in cloud-native architectures and multi-cloud deployment strategies
If there are standout projects that deserve spotlight:
From basic_info.md - keep concise.
Tone:
Language:
Formatting:
Before writing output:
Write to RESUME.md in the base directory.
what_i_did_2024.md: "Led backend migration to MongoDB"
performance_2024.md: "Query latency reduced by 40%, handled 10K QPS"
my_thoughts_2024.md: "Learned NoSQL data modeling, understood trade-offs"
- Architected and led critical backend migration from PostgreSQL to MongoDB, applying NoSQL data modeling principles to achieve 40% latency reduction while scaling to 10K+ queries per second
Notice how it:
You may receive additional instructions like:
Adapt synthesis strategy accordingly using your LLM judgment.