RESEARCH INTO PRACTICE
Field notes
Practical questions about AI, software, and the people doing the work.
I’m René Velarde. These notes bring together research, lessons from internal projects, and approaches I would try with a team. I keep examples, evidence, and open questions in view.
About the authorLessons from development 5 min read
Before you trust an AI workflow, decide what “done” means
Lessons from internal AI workflow projects: define a useful result, preserve uncertainty, make changes inspectable, and test the whole experience.
Choosing a first project 4 min read
Which nonprofit task should you try AI on first?
A practical way to choose a first nonprofit AI project: recurring work, clear sources, manageable consequences, and a person who can check the result.
Evidence and evaluation 4 min read
How do you tell whether AI is actually giving your team time back?
Measure AI time savings across the whole task, including preparation, review, corrections, and maintenance, while keeping quality and responsibility visible.
Communication and responsibility 4 min read
When AI writes your nonprofit’s story, who remains accountable?
A practical editorial review for nonprofit teams using AI to write: check the evidence, preserve people’s voices, clarify consent, and name the person approving publication.