Draft AI Architecture Evaluation Framework and Safety Protocols
Synthesise the workflow maps, model selections, and data pipelines into a unified technical blueprint. Define explicit evaluation metrics for accuracy, hallucination rates, and performance, alongside real-time monitoring and fallback mechanisms. Establish strict safety guardrails, input and output sanitisation, and ethical AI standards to mitigate risk.
Formulating this blueprint produces the central decision-ready artefact required to build, test, and audit the venture's AI capability. It gives engineering teams clear technical specifications while protecting the brand against algorithmic bias, toxicity, and hallucinations.
A formal AI Product Architecture document containing detailed pipeline schematics, model evaluation benchmarks, safety guardrail policies, and automated fallback logic. The document must explicitly define how hallucinations, bad outputs, and data leakage are prevented and handled in production.
Five questions an expert would ask when reviewing your output
Use these to challenge assumptions, pressure-test your logic, and check the quality of this action's output in the context of the parent task and wider venture development.
- 1
What specific automated guardrails exist to catch and intercept hallucinated or harmful model outputs before they reach the end user?
- 2
How do your evaluation metrics account for edge cases, adversarial inputs, and model drift over time?
- 3
What is the explicit fallback operational workflow when the primary AI model fails or returns a low-confidence score?
- 4
How does your safety architecture prevent prompt injection and proprietary context extraction attacks?
- 5
What quantitative benchmark determines that the AI system is performant enough to launch to live customers?