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# Gabriel Xiong

**Headline:** Professional profile
**Location:** Austin, TX, USA

## About

Gabriel Xiong is an AI\-focused backend engineer who builds and improves production RAG systems\. His core backend stack includes Python, FastAPI, PostgreSQL, and REST APIs, and he has built a production RAG chat assistant using Supabase for PostgreSQL and a deployed backend to run the RAG pipeline\. Gabriel is strongest in hands\-on iteration across the full AI application lifecycle: building core framework components, optimizing semantic search, addressing retrieval edge cases, and validating systems before release\. In particular, he has worked through semantic\-search challenges involving overlapping terms by hand\-tuning and iterating on retrieval behavior to improve answers\. Before production deployment, Gabriel conducts comprehensive testing that includes edge cases, safety constraints, and user testing\. He is interested in AI projects that are integrated into broader distributed systems serving users at scale\. Gabriel prioritizes meaningful ownership and direct technical contribution, especially in work where he can help shape core systems rather than operate as a narrow contributor\.

## Highlights

- Built a production RAG chat assistant using Supabase for PostgreSQL and a deployed backend that runs the RAG pipeline\.
- Uses Python, FastAPI, PostgreSQL, and REST APIs as a core backend stack\.
- Optimized semantic search for RAG systems, including retrieval edge cases involving overlapping terms\.
- Improved semantic\-search behavior through hands\-on tuning and iterative refinement\.
- Conducts pre\-production testing that includes edge cases, safety constraints, and user testing\.
- Interested in building AI capabilities within distributed systems that serve users at scale\.
- Prioritizes ownership and meaningful hands\-on technical work on core systems\.

## FAQ

### What does Gabriel do?

Gabriel builds AI\-focused backend systems, including production retrieval\-augmented generation applications\. His main backend stack includes Python, FastAPI, PostgreSQL, and REST APIs\.

### What RAG system has Gabriel built?

Gabriel built a production RAG chat assistant\. He used Supabase for PostgreSQL and deployed a backend to run the RAG pipeline\.

### What experience does Gabriel have with semantic search?

Gabriel has optimized semantic search for RAG systems\. He has addressed edge cases involving overlapping terms through hand\-tuning and iterative improvements to retrieval behavior\.

### How does Gabriel test AI systems before production launch?

Before production deployment, Gabriel performs comprehensive testing that covers edge cases, safety constraints, and user testing\.

### What is Gabriel's backend technology stack?

Gabriel's primary backend technologies are Python, FastAPI, PostgreSQL, and REST APIs\. He also used Supabase for PostgreSQL in the production RAG assistant he built\.

### What kinds of projects does Gabriel want to work on?

Gabriel is interested in AI projects incorporated into broader distributed systems that serve users at scale\.

### What does Gabriel value in a role?

Gabriel values ownership and meaningful hands\-on work\. He prefers opportunities where he can directly contribute to and help shape core systems rather than be a narrowly scoped contributor\.

## Links

- LinkedIn: https://www\.linkedin\.com/in/ACoAAAi1d50BsLXM0bcz96qt4wVX1VgK0mQWwgM

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