Diagnosing Your Current State

Use an outcome-first structure. Give ChatGPT the role, goal, approved source notes, constraints, and exact output you want back.

I am using Build The System for L&D systems work.

Prompt: Diagnosing Your Current State

Goal: Assess where an L&D function sits between content factory and capability infrastructure.

Audience: L&D teams that need a frank operating-model diagnosis before choosing changes.

Source material I will provide:
- Current measures
- Business partner behavior
- SME collaboration pattern
- AI currently in use
- Where most team time goes

Inputs to use: [measures], [partner engagement], [SME workflow], [AI usage], [time distribution]

Working notes:
- measures: [add measures]
- partner engagement: [add partner engagement]
- SME workflow: [add SME workflow]
- AI usage: [add AI usage]
- time distribution: [add time distribution]

Rules:
- Use only approved source material.
- Do not invent policy, compliance, learner, employee, customer, financial, or business facts.
- Do not paste sensitive, confidential, employee, learner, customer, or proprietary data into an AI tool unless your organization has approved that tool for that data.
- Treat AI output as a draft that needs human review.

Return:
1. Current-state assessment
2. Identity implications
3. Two or three highest-leverage changes

Human review checklist:
- Does the output stay inside the source material?
- Are assumptions and missing information labeled?
- Is there a clear human owner for the final decision?
- Does the output need legal, compliance, privacy, accessibility, leader, requester, or SME review?

Use labeled sections so context, source material, task, constraints, and output format stay separate.

<context>
I am using Build The System for L&D systems work.
Prompt: Diagnosing Your Current State
Use case: Assess where an L&D function sits between content factory and capability infrastructure.
Audience: L&D teams that need a frank operating-model diagnosis before choosing changes.
</context>

<source_material_to_prepare>
- Current measures
- Business partner behavior
- SME collaboration pattern
- AI currently in use
- Where most team time goes
</source_material_to_prepare>

<inputs>
- measures: [add measures]
- partner engagement: [add partner engagement]
- SME workflow: [add SME workflow]
- AI usage: [add AI usage]
- time distribution: [add time distribution]
</inputs>

<task>
Use the source material and inputs to produce a practical, review-ready output for this L&D workflow. Stay inside the source material. If a detail is missing, mark it as "Needs confirmation" instead of inventing it.
</task>

<constraints>
- Use only approved source material.
- Do not invent policy, compliance, learner, employee, customer, financial, or business facts.
- Do not paste sensitive, confidential, employee, learner, customer, or proprietary data into an AI tool unless your organization has approved that tool for that data.
- Treat AI output as a draft that needs human review.
</constraints>

<output_format>
1. Current-state assessment
2. Identity implications
3. Two or three highest-leverage changes
</output_format>

<human_review>
- Does the output stay inside the source material?
- Are assumptions and missing information labeled?
- Is there a clear human owner for the final decision?
- Does the output need legal, compliance, privacy, accessibility, leader, requester, or SME review?
</human_review>

Use goal, source, expectations, and output. For Obsidian context, use approved excerpts, Word summaries, OneDrive files, SharePoint pages, Teams context, or Outlook threads.

Goal: Help me complete Diagnosing Your Current State for an L&D workflow.

Source: Use only the selected Microsoft 365 document, meeting notes, email thread, spreadsheet, SharePoint file, or pasted notes I provide. Do not use unsupported assumptions.

Context:
- Use case: Assess where an L&D function sits between content factory and capability infrastructure.
- Audience: L&D teams that need a frank operating-model diagnosis before choosing changes.
- Source material to have ready:
- Current measures
- Business partner behavior
- SME collaboration pattern
- AI currently in use
- Where most team time goes

Inputs:
- measures: [add measures]
- partner engagement: [add partner engagement]
- SME workflow: [add SME workflow]
- AI usage: [add AI usage]
- time distribution: [add time distribution]

Expectations:
- Use only approved source material.
- Do not invent policy, compliance, learner, employee, customer, financial, or business facts.
- Do not paste sensitive, confidential, employee, learner, customer, or proprietary data into an AI tool unless your organization has approved that tool for that data.
- Treat AI output as a draft that needs human review.

Output:
1. Current-state assessment
2. Identity implications
3. Two or three highest-leverage changes

Before finalizing, add a short "Needs human review" section covering:
- Does the output stay inside the source material?
- Are assumptions and missing information labeled?
- Is there a clear human owner for the final decision?
- Does the output need legal, compliance, privacy, accessibility, leader, requester, or SME review?

Use a task-first structure with clear input labels, one example pattern, rules, and a concise output format. For Obsidian context, use approved excerpts, Drive exports, Google Docs, or NotebookLM source sets.

Task: Complete Diagnosing Your Current State for an L&D workflow.

Use case:
Assess where an L&D function sits between content factory and capability infrastructure.

Audience:
L&D teams that need a frank operating-model diagnosis before choosing changes.

Input labels to use:
- measures: [add measures]
- partner engagement: [add partner engagement]
- SME workflow: [add SME workflow]
- AI usage: [add AI usage]
- time distribution: [add time distribution]

Source material to prepare:
- Current measures
- Business partner behavior
- SME collaboration pattern
- AI currently in use
- Where most team time goes

Example pattern:
Field: Missing information
Good answer: Name the specific detail to confirm, who can confirm it, and why it affects the next decision.

Rules:
- Use only approved source material.
- Do not invent policy, compliance, learner, employee, customer, financial, or business facts.
- Do not paste sensitive, confidential, employee, learner, customer, or proprietary data into an AI tool unless your organization has approved that tool for that data.
- Treat AI output as a draft that needs human review.

Return:
1. Current-state assessment
2. Identity implications
3. Two or three highest-leverage changes

Human review checklist:
- Does the output stay inside the source material?
- Are assumptions and missing information labeled?
- Is there a clear human owner for the final decision?
- Does the output need legal, compliance, privacy, accessibility, leader, requester, or SME review?