Applied AI prototypes, from crops to small models
Computer vision for crop disease and a policy chatbot for a federal department, AI prototypes for small businesses at the Vector Institute, and fine-tuning and RAG evaluation on my own time.
- Result
- 25% → 75%
exact-match extraction accuracy after QLoRA fine-tuning of Llama 3.2 - Role
- AI developer, technical assistant and independent builder
- Domain
- Public sector, small businesses and independent work (Research and public sector)
- Period
- 2023 – present
Work
- Agriculture and Agri-Food Canada (2023). Computer vision for early crop-disease detection, and a chatbot that answers farmers' policy questions.
- Vector Institute (2024). Helped small and mid-sized businesses take AI use cases from problem definition to a prototype in three months.
- Small-model fine-tuning. Fine-tuned Llama 3.2 with QLoRA on consumer hardware for structured data extraction. Exact-match accuracy went from about 25% to about 75%.
- RAG evaluation lab. An open workspace for benchmarking RAG pipelines before production, which cut manual evaluation time by 60%.
- Research. Five peer-reviewed papers in fraud detection, computer vision and healthcare prediction.
Capabilities
Stack
- Python
- Computer vision
- Llama 3.2
- QLoRA
- RAG
Skills shown
- Python
- GraphRAG and RAG
- Agent evaluation and LLM-as-judge
From the log
- 15 Jan 2024
Took small-business AI use cases from problem to prototypeEarly practice in what solution work really is. Most of it is scoping the problem so a model can actually help. - 15 May 2023
Built crop-disease detection and a policy chatbotVision models and a retrieval chatbot over policy documents. My first RAG system, in a public-sector setting.