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AINLPRAGProductcompleted
SkillGap AI
Semantic embedding and RAG engine for resume and job description fit analysis.
01 · The Problem & Hypothesis
Why this needed to be built
A résumé and a job description can superficially look like a match while still concealing critical capability gaps, leading to poor hiring signals and unfocused career preparation.
The Core Idea
Use semantic vector embeddings and Retrieval-Augmented Generation (RAG) to measure genuine competency fit, detect latent skill gaps, and generate structured learning roadmaps.
02 · Technical Implementation
How the system was architected
Built to explore applied natural language processing and semantic search beyond generic chatbot wrappers. The platform extracts entities from resumes, maps them against industry skill taxonomies, and produces detailed diagnostic gap analyses with actionable bridge roadmaps.
Python
FastAPI
RAG & Vector Embeddings
Celery
Redis
React
Supabase
TypeScript
03 · Key Takeaways Earned
Takeaway 01
“AI is valuable when applied to domain-specific analytical problems rather than generic conversational prompts.”
Takeaway 02
“Taxonomy normalization and chunking strategies constitute 80% of building reliable semantic RAG systems.”