What you will learn
User training, Trust calibration, Feedback loops, Demos, Executive summaries.
Engineering the capability
Adoption depends on whether people understand the system’s role in their work. Training should show ordinary success, uncertainty, an incorrect output, and recovery. A demo containing only flawless answers teaches users the wrong expectations.
Explain which actions remain human-owned and how evidence can be inspected. Give users a clear feedback path tied to a case or request ID. Collect reasons for rejection rather than only a thumbs-down count: wrong policy, missing context, poor wording, and workflow friction need different fixes.
Adapt communication to the audience. Operators need troubleshooting and control details; sponsors need progress against agreed outcomes and remaining decisions. Keep metrics honest about sample sizes and exclusions. A compelling story should be supported by evidence, including limitations.
Worked case
Users reject a draft because the tone is wrong, but its facts are correct. Treating every rejection as factual failure wastes effort on retrieval. Add rejection reasons and review examples with users. The next improvement may be a style control or better editing workflow rather than another model.
Put it into practice
Continue with the next lab: prepare an evidence-based customer demo. Build the artifact, record the failure cases, and explain the tradeoff before moving on.