RAG and Retrieval Quality
Chunking strategies, hybrid retrieval, reranking, and citation-grounded response patterns.
About
I Close the Gap Between AI Demos and Production Systems.
Most AI features look great on demo day. I work on the part that comes after; making them reliable, measurable, and safe to keep in production a month later.
What pulled me toward this specifically was a pattern I kept seeing; AI features that looked great in the demo, then quietly became a trust problem once real users hit them. Nobody could explain why because nobody had been measuring. I built my whole workflow around making sure that doesn't happen.
- Before I write a prompt: I define what success looks like and where the data comes from.
- While I build: I wrap model behavior in guardrails so it stays predictable under pressure.
- Before I ship: I score it, compare it to the last version, and only release if it passes.
What I'm Actually Good At
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-first APIs and workflow systems; the tools I reach for when something needs to ship fast and stay maintainable.
Python
FastAPI
Pydantic
n8n
Docker
Data + AI Layer
Everything needed to make retrieval accurate, answers grounded, and quality measurable before release.
Postgres
pgvector
OpenAI APIs
Reranking
Eval Harnesses