RAG and Retrieval Quality
Chunking strategies, hybrid retrieval, reranking, and citation-grounded response patterns.
About
AI Application Engineering With Product Discipline.
I build systems where answers are useful, grounded, and observable in production. The focus is not demo quality, but release quality.
- Design Principle: define the data and reliability contract before prompt tuning.
- Build Principle: deterministic interfaces around LLM behavior.
- Release Principle: evaluate, compare, and ship only behind quality gates.
What I Bring
Core Strengths
LLM Workflow Engineering
Prompt contracts, function-calling, fallback behavior, and stable API outputs for apps.
Evaluation and Reliability
Regression suites, faithfulness tracking, and trace-based debugging in pre-release workflows.
Stack
Technologies I Use Most
Application Layer
Python-based API and workflow systems for shipping product features quickly.
Python
FastAPI
Pydantic
n8n
Docker
Data + AI Layer
Storage, retrieval, orchestration, and evaluation tooling required for production reliability.
Postgres
pgvector
OpenAI APIs
Reranking
Eval Harnesses