Methods
How we keep AI work dependable.
The practices behind every engagement, and the results that shaped them.
AI production operations
Multi-model work, managed like a program
- Practice
- Treat several AI models as a production team with clear roles, phase gates, a decision log, documented handoffs and a release process.
- Result
- A 113-module interactive project delivered in about three weeks across five AI models.
- Why it matters
- AI output scales quickly; without gates and owners, so do mistakes.
Workflow quality checks
Verify outcomes, not status
- Practice
- Audits that check what a workflow actually produced, not what it reported about itself.
- Result
- Caught six of nine features doing nothing while every automated test reported success.
- Why it matters
- Teams relying on AI-generated work need checks that can't be fooled by a green status.
Documented methods
Repeatable by design
- Practice
- Goals, quality checks and handoffs written down so work can be resumed or handed over without rework.
- Result
- A method refined across 24+ build sessions and 45 documented releases of a web application.
Learning design
Training people finish
- Practice
- Start from what people need to do, not what they need to be told: action mapping, Bloom's taxonomy, practice on real tasks.
- Result
- Onboarding modules with branching scenarios and narrated audio, built around observable skills.
Human review
Review points by risk
- Practice
- Spot checks for low-risk steps, approval for anything that leaves the team, no automation for decisions about people's eligibility, money or rights.
- Read more
- Human-in-the-Loop AI: Why Review Points Matter
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