Reliability & Prompt Engineering
AI hallucinating in a sensitive context is not just a bug. It is an operational, legal, and ethical risk.
Language models are powerful — and fallible. In standard contexts, an AI error is inconvenient. In fields like healthcare, research, law, or personal data management, it can have real-world consequences for decisions, individuals, and organizations.
Reliability in an AI system is not something you declare. It is built upfront with rigorous methodology: well-structured prompts, verification protocols, and safeguards adapted to the context. This is what I call serious prompt engineering — not just optimizing phrasing, but engineering for reliability.
My approach is based on rigorous methodological principles, inspired by scientific research standards: graded evidence matrices, anti-hallucination protocols, source structuring, and output traceability.
How I support you
- Design of robust and reproducible prompts for professional use
- Implementation of anti-hallucination protocols tailored to your context
- Methodological structuring of AI workflows in sensitive contexts
- Audit of your existing prompts and AI practices