---
name: learn-do-teach-apprentice-model
description: "Help a teammate learn a job well enough to show someone else."
category: Strategy & Measurement
stage: —
definitive_article: /9-triangles-framework-scalable-home-service-businesses/
status: complete
---

# Learn, Do, Teach apprentice model

Help a teammate learn a job well enough to show someone else. This guide turns reading into practice and checks the work at each step. Start with one real task, its guide and a person who can review the result.

**The path:** Learn the method → Do checked work → Teach another person → Verify

**Start when:** A learner is building competence in one scoped task and a qualified reviewer is available.

## Inputs

- One canonical task recipe, its current revision and relevant real examples.
- An authorized practice task with real inputs and safe limits; a capable reviewer and later learner.
- [Learn, Do, Teach](https://blitzmetrics.com/learn-do-teach/) source and the task’s actual quality checks.

## Steps

1. Define the skill to learn and its observable pass criteria. A learner status or a completed document is not evidence of task competence.
2. Learn: read the recipe and proof examples, then explain the method and expected result back to the reviewer. List questions and source gaps before dependent actions.
3. Do: execute a bounded, authorized real task using the guide. Agree an early direction check before the full build and retain inputs, output and actual reviewer feedback.
4. Correct defects and repeat suitable practice until the agreed competence evidence is met. This library’s prior checklist asks for two consecutive clean real runs; keep that as an explicit local training gate, not a universal number claimed by the framework.
5. Teach: help another learner perform the task from the same maintained guide. Observe that learner’s result against the checks. Writing a lesson or meta article alone does not prove the second person learned the task.
6. Record each actual execution and the learner’s current stage, including incomplete teaching. The reviewer decides any change of review duty or responsibility; a successful demonstration does not grant new account access.
7. Use recurring questions to improve the recipe through its existing review process. Write each real run’s meta article; any public example requires separate release authority.

## Definition of done (QA checklist)

Quality assurance (QA) means checking the result against its agreed requirements; see the [Article Guidelines](https://localservicespotlight.com/article-guidelines/).

- [ ] Learning questions and actual practice evidence are retained.
- [ ] The agreed clean-run gate and the next learner’s result are verified or explicitly unfinished.
- [ ] Training status and responsibility reflect the reviewer’s real decision.

## Example(s)

**Fictional teaching example. This is not a client result or proof of a completed run.**

A fictional apprentice checks one service page with a reviewer. The first attempt misses a broken link; the next two pass the agreed checks. The apprentice then teaches a colleague, whose first check misses an image defect. The record says Do passed and Teach still in progress; the lesson write-up does not close that gap.

## Handoff and Content Factory context

[Manage the next assignment](https://local-service-spotlight.github.io/task-library/?task=management-communicate-iterate-delegate#task-management-communicate-iterate-delegate) receives the demonstrated scope and unresolved training needs.

This work supports the [Content Factory: Produce, Process, Post and Promote](https://blitzmetrics.com/content-factory/) across its relevant stages. It uses the actual inputs and next owner above; a strategy or measurement guide does not manufacture transcripts, clips or other unrelated stage outputs.

## Start with an agent

Give the AI worker this recipe, the real Inputs, the desired result and the actions already authorized. Ask for the saved output, source evidence, passed and failed checks, and the next owner. A skill is a set of instructions. Loading it does not prove access, install a scheduled job or complete the work. Use the [current installation guide](https://localservicespotlight.com/install/) only if reusable setup is needed; a ZIP contains instructions and is not an access grant.

Use the actual supported app and verified source access. A model name is not a promise of browser control, memory or unattended work. For recurring work, inspect the real job’s trigger, owner, timezone and observed firing; this recipe creates no schedule. Before any media playback, mute the player and set volume to zero. A silent review does not prove sound was heard.

## Record the real execution

Open the run record when work begins. Keep one execution ID, the exact starting recipe revision, input sources and actual state. Write a [meta article, the record of one real execution](https://blitzmetrics.com/meta-article-prompt/) with results, checks, failures and next owner. Link it to this task in the [Task Library](https://local-service-spotlight.github.io/task-library/). Writing is required; public release follows existing authority.

Reuse the same execution ID for internal reviews, meta writing, revisions and retries. A blocked run stays open with its dependency and owner, without an invented finish time. Keep historical public examples as dated evidence; article counts alone are not verified execution frequency. Use supported findings to propose and verify a recipe improvement.


<!-- factory-layer:start -->

## Factory chain (do not treat this as isolated)

- **Phase:** —
- **Importance:** 3/5 `●●●○○` — frequency 3, revenue 3, gating 2 (supporting)
- **Suggested previous station:** —
- **Suggested next station:** —
These stations are generated neighbors, not verified prerequisites or handoffs. Follow the task’s actual Inputs and Handoff sections.
- **Handoff:** Save this task’s actual output, source, date and checks where the receiving owner can read them. Follow the Inputs and Handoff sections for its next step.

## Model routing (same factory, any engine)

- **This task's lane:** `any` — Any capable chat model (Claude, ChatGPT, Grok, Gemini). SOP following, drafts, checklists.
- **Single-engine path:** Use the capable AI app you already have. Check its required tools and access before starting. Pass the actual result and proof to the next task.
- **Optional multi-engine:** A tested local model may draft; a stronger model may review facts and voice. Use supported tools for approved actions. Pick models from observed quality and cost; a model name does not prove it can run this task.
- **Never** store the working state in one vendor's memory. Files in the Content Library are the bridge.

<!-- factory-layer:end -->
## Definitive article & links

- [Maintained source guide](https://blitzmetrics.com/9-triangles-framework-scalable-home-service-businesses/)
- [This task in the Task Library](https://local-service-spotlight.github.io/task-library/?task=learn-do-teach-apprentice-model#task-learn-do-teach-apprentice-model)
- [Article Guidelines](https://localservicespotlight.com/article-guidelines/)
- [Definitive article and task recipe standard](https://blitzmetrics.com/definitive-article-guide/)
- [How recipes and run records work together](https://localservicespotlight.com/meta-articles/)

## Review and evidence still needed

The inherited contributor status is `complete`. It is preserved, not promoted by this rewrite. That label alone does not verify document readiness, account access, an actual execution or a client outcome.

A relevant real execution, source evidence, independent reviewer and accepted handoff remain to be recorded. The fictional example teaches the method and does not fill a real-run evidence gap. The opening and full method need a named semantic reviewer; the task-specific path is source guidance, with visible desktop/mobile presentation still to check.