1. Define the task
Be specific about what the system should help with, what remains out of scope and what a useful result looks like.
Responsible AI
Practical AI With Clear Guardrails
Useful AI work starts with a real problem, appropriate data, measurable evaluation and clear human responsibility. This page describes the implementation principles used to structure AI discussions and projects.
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Implementation principles
Six questions to answer before relying on an AI feature
These are engineering and planning principles, not a compliance framework, certification or guarantee.
2. Minimize data
Use only the data needed for the task and separate public, internal, personal and confidential information appropriately.
3. Protect access
Consider authentication, authorization, secrets, document permissions and data exposure before connecting models to systems.
4. Keep human responsibility
Decide where a person must review, approve, correct or reject AI-generated output, especially when consequences are meaningful.
5. Evaluate real outputs
Test representative cases, failure modes and unacceptable answers instead of judging only a polished demonstration.
6. Operate and improve
Plan for monitoring, model or prompt changes, cost, latency, fallback behavior and ongoing review after launch.
AI delivery path
From idea to a controlled production feature
The exact stack depends on the use case. The sequence below keeps the decision process technology-neutral.
Problem
Define the user, task and expected outcome.
Data
Identify sources, permissions and sensitivity.
Prototype
Validate the workflow before overbuilding.
Evaluate
Test useful, incorrect and unsafe outcomes.
Integrate
Connect software, controls and human review.
Let’s work together
Have an AI use case in mind?
Describe the workflow, users, data and desired outcome. We can decide whether AI is actually the right tool after the requirement is clear.