Before you start
A chat assistant, a familiar work task and a fictional source brief.
Why this lesson exists
This lab adapts the “Build an AI Training Playbook for Real Estate” project write-up into a practice build. The time is an estimated first session, not a promise to finish a production system. Use the public repository as a reference when available; the exercise can be built with original sample content.
Do the exercise
Define the practice version
Pick one outcome, such as drafting a factual description. Show weak and improved prompts, then ask the learner to check the output against the brief.
Build step 1
Pick one job outcome, such as writing a listing description.
Build step 2
Teach a reusable prompt formula.
Build step 3
Show a bad input, improved input, and reviewed output.
Build step 4
Add a compliance checklist beside the exercise.
Build step 5
Store prompts as structured data with categories and tags.
Build step 6
Add search and one-click copying.
Run the experiment
Give a colleague a new fictional example. Observe whether they can adapt the prompt and catch an intentionally unsupported claim without help.
A prompt to adapt
Replace the bracketed parts with your own practice details.
Work in a disposable practice project. Explain any setup requirements before changing files. Build one small step at a time and show how I can check it. Create a beginner lesson that teaches a real-estate agent to draft property marketing with AI. Include learning objective, five-minute explanation, worked example, copyable prompt, review checklist, Fair Housing caution, and practice assignment. Review this property description for unsupported claims, protected-class implications, neighborhood stereotyping, invented amenities, ambiguous pricing, and language that should be verified before publication. My first-version boundary: Pick one outcome, such as drafting a factual description. Show weak and improved prompts, then ask the learner to check the output against the brief.
Run this experiment
Give a colleague a new fictional example. Observe whether they can adapt the prompt and catch an intentionally unsupported claim without help.
Check your result
Use evidence from your output. A confident explanation from the AI is not enough.
- The learner produces a concrete artifact.
- The exercise has an answer key or review criteria.
- Industry-specific claims receive the appropriate human review.
If it isn’t working
Do not teach a prompt as a compliance guarantee. If the task involves regulated advertising, use current authoritative guidance and a qualified reviewer.
Optional. Progress stays in this browser.
Where this came from
Public project repository ↗. The practice lesson is an adaptation, not a verbatim transcript. About the sources.
Prepared September 2026. Tools and interfaces change; use current official setup instructions. Session lengths are estimates.