Resources

Microsoft, EMNLP 2024. The head-to-head comparison that showed RAG at 87.5% accuracy vs fine-tuning at 50.4%. The paper that validated our pivot.

Allen-Zhu & Li, ICLR 2025. Proved that each fact needs 100-1,000 training exposures to be reliably learned. We gave ours about 15. Mystery solved.

Zhou et al., 2023. Showed that fine-tuning primarily teaches style and format, not new knowledge. The model learns how you talk, not what you know.

Stanford, 2024. Found a 37% ceiling on new knowledge absorption through fine-tuning even with commercial APIs. The problem isn't technique, it's approach.

SMU/Microsoft, 2024. A hybrid fine-tuning + RAG approach. Interesting research, but pure RAG proved simpler and sufficient for our use case.

Run large language models locally. The engine behind our AI inference. Download a model, run it, talk to it through an API. That simple.

A self-hosted chat interface for AI. This is what our clinicians actually interact with. Clean UI, conversation history, file uploads.

A real-time web framework for Perl. The backbone of RAG Proxy. Handles HTTP interception, async requests, and WebSocket connections.

Fast numerical computing for Perl. Does the vector math: cosine similarity calculations across thousands of document embeddings in milliseconds.

A serverless database engine. Stores our document chunks and metadata. No daemon, no config, no port. Just a file. Perfect for our scale.

BAAI's multilingual embedding model. Converts text into 1,024-dimension vectors. Runs locally through Ollama. Handles English, French, and mixed-language queries.

A tiny SQLite wrapper for Mojolicious. Gives us migrations, connection pooling, and a clean query interface. The glue between Brain.pm and the database.

University of Toronto's annual IT knowledge-sharing conference. Where this talk is being presented, May 6-7, 2026.

Canada's largest dental faculty. Home to the RAG Proxy system, where clinicians and staff use it daily for policy lookups.