Skip to content
GuidesReal EstateMarketingFamilyLive EventseSports

Build-first teaching system

Design the workshop backward from useful work.

A strong AI workshop does not end with people impressed by a demo. It ends with a participant-built artifact, a review standard, and a next test they can run at work.

By Ryan WannerPractical guideReviewed August 20, 2026

The 60-minute backward-design map.

  1. 01

    0–5 · Name the win

    Show the exact take-home artifact and the observable standard it must meet.

  2. 02

    5–15 · Model the method

    Demonstrate task, context, constraints, output, and one correction using safe data.

  3. 03

    15–35 · Participant build

    People adapt the method to their own bounded task while the facilitator coaches the work.

  4. 04

    35–48 · Review in pairs

    Use the same rubric to identify unsupported claims, missing context, and unclear output.

  5. 05

    48–56 · Revise and rerun

    Make one targeted correction and compare the two outputs against the source.

  6. 06

    56–60 · Save the receipt

    Package the prompt, input, output, review notes, and next real-world test.

Generate the lesson plan from the work.

Backward workshop designer

Design a build-first AI workshop around one real task.

AUDIENCE
[role, experience, and what they already know]

REAL TASK
[one job they already perform]

FINISHED ARTIFACT
[the exact file, prompt, checklist, or workflow they leave with]

SAFE DEMO INPUTS
[fictional, public, or sanitized source material]

TIME AVAILABLE
[minutes]

Return: a measurable outcome, minute-by-minute agenda, facilitator demo, participant build, review rubric, likely failure points, accessibility accommodations, and the final take-home package. Keep tool explanation under 20% of the session. Do not invent policy, capability, or outcome claims.

Situation

Listing-description lab

Eight agents already know their listing facts but write inconsistent drafts and occasionally lose the source trail.

Useful output

Each participant leaves with a fact-locked listing prompt, one source-backed draft, and a five-item compliance review.

Completion receipt

Saved source sheet, prompt version, first draft, corrected draft, rubric score, and named next listing test.

Keep building the system.

Prompt literacy

Teach the portable anatomy behind a useful instruction.

Open standalone guide →

Evaluation rubric

Score useful work without turning the class into a quiz.

Open standalone guide →

AI teaching hub

Use the complete 60-minute facilitation system.

Open standalone guide →

Research sources

Check the current rules, capabilities, and source basis.

  1. 01UNESCO: AI Competency Framework for Teachers ↗

    A human-centered competency framework spanning AI foundations, ethics, pedagogy, and ongoing professional learning.

  2. 02NIST: AI Risk Management Framework Core ↗

    The Govern, Map, Measure, and Manage framework, including training, intended-use, human-oversight, testing, and documentation outcomes.

  3. 03OpenAI Academy: Prompting ↗

    First-party prompting guidance for clear tasks, useful context, desired output, and iterative refinement.

Product access, limits, local listing rules, platform policies, and laws can change. Re-check the linked authority before important or public work.

Keep building

Check us out on Skool for 30+ classes and growing.

AI Acceleration: Real Estate is a private, self-directed curriculum for practical AI work across content, communication, lead follow-up, CRM, client research, operations, and relationship management. Build it. Review it. Keep what works.

Explore the Skool community ↗

Follow Ryan + AI Acceleration

No ranking, revenue, closing, or time-saving promise. The classes teach the work and the review systems around it.