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Shashank SinghAI/ML Developer

AI Engineer building production-ready RAG systems, AI agents, LLM applications, FastAPI backends, and intelligent automation platforms.

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Research Notes

Focused research notes for the strongest AI systems.

ATS Resume AI and SQL Copilot are documented with abstract, research question, methodology, architecture, evaluation plan, limitations, future work, and GitHub evidence.

Research Note 01

ATS Resume AI

An applied RAG system for resume analysis, ATS scoring, and job-fit feedback using local model components and retrieval.

Research question: How can an AI resume analyzer provide useful ATS feedback while keeping recommendations grounded in the user's actual resume and target role?

PythonFastAPIRAGChromaDBSentence TransformersLocal Models
Method

Parse resume content into section-level units before scoring.

System

Resume Upload -> Parser -> Rule Checks

Impact

Turns resume analysis into a structured optimization workflow.

Read Case StudyGitHub
PythonSystem View
ParserATS RulesEmbeddingsFeedback
AI Resume IntelligenceATS Resume AI

Architecture

Resume Upload
Parser
Rule Checks
Embedding Index
Retrieved Guidance

Research Note 02

SQL Copilot

A database copilot that frames natural-language analytics as a guarded planning, SQL generation, validation, and explanation loop.

Research question: How can a natural-language SQL assistant reduce friction for analytics while making generated queries safer and more explainable?

PythonSQLLLMPrompt EngineeringValidationVercel
Method

Classify the analytical intent before SQL generation.

System

User Question -> Intent Planner -> Schema Context

Impact

Makes database exploration more accessible.

Read Case StudyGitHub
PythonSystem View
PlannerSchemaSQL GuardExplain
AI CopilotSQL Copilot

Architecture

User Question
Intent Planner
Schema Context
SQL Draft
Validation