AI application developer — RAG & agents
- CHF 86-CHF 138/hr
- Occasional
- 13 years
- RAG pipelines
- LLM evals & guardrails
- Chatbot development
- LLM API integration
- Supabase
- Vector search & embeddings
- Prompt engineering
Projects 3
Documentation assistant
Part of a team of three
RAG over 4k support articles with a 120-case eval set. Answer accuracy 71% → 89% after chunking and reranking changes.
- vector-search
- chatbots
- prompt-engineering
- rag
Support bot with guardrails
Part of a team of three
Refusal and escalation rules so the bot declines policy questions instead of inventing answers.
- prompt-engineering
- vector-search
- chatbots
- supabase
Documentation assistant (second engagement)
Sole developer
RAG over 4k support articles with a 120-case eval set. Answer accuracy 71% → 89% after chunking and reranking changes.
- prompt-engineering
- vector-search
- supabase
- llm-integration
About
Retrieval systems that can be evaluated rather than demoed. pgvector on Supabase, LLM APIs, retrieval tuning, and an eval harness so improvements are provable. I care about refusal behaviour as much as recall.