> 🌐 中文版本 / Chinese version: [L1_OPENWEBUI_COURSE_PLAN.md](L1_OPENWEBUI_COURSE_PLAN.md)

# L1 OpenWebUI Enterprise Enablement Course Plan

Version: v1.0
Author: Morris Lu (盧業興) · Tiger AI 虎智科技
Applicable Level: L1 Controlled AI Access
Reference Video Playlist: DigitalBrainBase OpenWebUI playlist
`https://www.youtube.com/watch?v=oXJ4L1G8kaI&list=PL_rTgQnnMXsXAsEiid-tWhaj03SsP4U5Z`

---

## 1. Repositioning L1

L1 is not merely about teaching employees "how to chat with AI." What enterprises actually need is a governed AI entry point:

> Every employee can sign in to OpenWebUI with their own account and have a personal chat area, conversation history, and individual settings; administrators can manage accounts, roles, groups, models, permissions, features, and data-use boundaries.

Therefore, the L1 course must serve two audiences simultaneously:

| Audience | What They Need to Learn |
|---|---|
| General users | Sign in, choose a model, start a new chat, manage their own conversations, upload permitted data, and use prompts to complete work |
| Admin / IT / HR | Create or review accounts, configure roles, groups, default permissions, model visibility, file upload, Web Search, API Keys, sharing, and data policies |

The completion criterion for L1 is not "everyone can ask questions," but rather "the company can let employees begin using AI in a safe, manageable, and scalable manner."

---

## 2. Essential Enterprise Capabilities

| Capability | Enterprise Requirement | Acceptance Method |
|---|---|---|
| Account sign-in | Each employee signs in with their own account; no shared accounts | Test account list, sign-in screenshots |
| Personal chat area | Each user has their own chat history, folders, personal prompts, and settings | Individual user operation demos |
| Role management | At minimum Admin, User, Pending; new accounts require review or controlled activation | Admin Panel configuration screenshots |
| Group management | Create groups by department or seed team | Groups configuration sheet |
| Permission control | Govern file upload, Web Search, Image, Tools, API Keys, sharing, deletion, and export | Permissions checklist |
| Model control | Different groups can access different models or model presets | Model access sheet |
| Data boundaries | Define which data may be input, must not be input, or requires human confirmation | AI usage policy |
| Usage records and governance | Administrators know how to audit settings, publish policies, and resolve account or permission issues | L1 Admin Runbook |

Note: According to the official OpenWebUI documentation, its RBAC is composed of three layers — Roles, Permissions, and Groups — and permissions follow an additive model. Enterprises should therefore adopt the principle of least privilege, first tightening Global Defaults and then opening advanced features through Groups.

Official documentation references:

- Roles: `https://docs.openwebui.com/features/rbac/roles/`
- Permissions: `https://docs.openwebui.com/features/rbac/permissions/`
- Groups: `https://docs.openwebui.com/features/rbac/groups/`

---

## 3. Learning Objectives

> Written per the Bloom formula in [`ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md`](ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md) §4: **[Verb] + [Content] + [Context]** — assessable and measurable.

### 3.1 Four primary LOs for the CLP (the promise learners see before enrolling)

By the end of the L1 course, learners can:

1. **Apply** OpenWebUI's three-layer RBAC (Roles / Permissions / Groups) to **design** a permission matrix for their own department (least privilege → group escalation), **producing** an auditable Admin Panel configuration screenshot set. (Apply)
2. **Execute** AI-accelerated workflows for the 4 high-frequency tasks (Email / meeting minutes / summary / report draft), **publishing** 5 prompt templates to a personal Prompt Library. (Apply)
3. **Identify** the 4 data sensitivity tiers (public / internal / sensitive / forbidden) for AI input, **judging** when data can be pasted, must be de-identified, or requires human confirmation; **producing** a 1-page AI usage policy draft. (Analyze)
4. **Produce** an L2 Skill candidate list (≥ 5 prompt patterns observed in actual use), **validating** each with repeatability + ROI evidence and ranking against L2 upgrade triggers. (Evaluate)

### 3.2 Detailed capability list (section mapping)

Each primary LO is supported by multiple detailed capabilities throughout the course:

| Primary LO | Supporting capabilities |
| --- | --- |
| LO1 (permissions) | Login / model switch / new chat; Admin creates accounts / roles / groups; configure model visibility & feature permissions |
| LO2 (high-frequency work) | Build personal chat area (folders / prompts / files); use file upload or Knowledge for low-sensitivity Q&A; understand model selection / parameters / hallucinations / human confirmation |
| LO3 (data boundary) | Define inputtable / forbidden / human-confirmation-required data; produce AI usage policy and Gate 1 acceptance checklist |
| LO4 (L2 bridge) | Observe and record L1 usage patterns; ROI-evaluate candidates; deliver to L2 course as Skill candidates |

### 3.3 Interactive Learning Design (Engagement + Formative + Summative)

> Per [`ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md`](ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md) §5.1 (three-act structure) + §7 (interactive types) + §9.4 (interactive checklist).

**Engagement activity (within first 10 min of intro):**

> **Each learner logs in, opens a new chat, picks a model, and uses one Prompt to complete a small piece of their actual day's work**. 10 min hands-on, not lecture. Goal: turn "AI as abstract concept" into "AI is right here I can use it now" as concrete experience.

**Formative gates (quick self-checks at end of each section):**

| Section | Formative check | Duration |
|---|---|---|
| §6.1 each topic | 1 mini-demo: learner completes 1 high-frequency task with a Prompt on the spot | 5 min |
| §6.2 each topic | 1 config screenshot comparison: learner's Admin Panel vs reference | 5 min |
| Data boundary topic | 10 data-case judgment questions (inputtable / de-identify / forbidden) | 10 min |

**Summative gate (end of course):**

Maps to **Gate 1** (§9). 6 deliverables:
1. Personal Prompt Library v1 (≥ 5 prompts)
2. High-frequency work evidence (≥ 3 of Email / meeting minutes / summary / report)
3. Admin Panel configuration screenshots (roles + groups + permissions + models)
4. AI usage policy draft (1 page)
5. Data case judgment sheet (10 cases, 100% correct)
6. L2 Skill candidate list (≥ 5 with ROI evidence)

### 3.4 Reference materials list

Per [`ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md`](ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md) §9.6 (completeness), the course provides the following downloadable materials:

| Type | Location | Use | Status |
|---|---|---|---|
| User operation manual | §10 Deliverables (PDF TBD) | login SOP + model selection decision tree + file upload checklist + 10 data boundary cases | ☐ TBD PDF |
| Admin Runbook | §10 Deliverables (PDF TBD) | full setup steps for accounts / roles / groups / permissions / models / features | ☐ TBD PDF |
| Prompt Library template | §6.1 (5 example prompts embedded in lectures) | Email / meeting minutes / summary / report / FAQ | ☑ Available |
| Data boundary quiz bank | §3.3 Formative gate (10 cases) | Learner quiz | ☐ TBD |
| Gate 1 acceptance checklist | §9 (6-item checklist) | Summative gate | ☑ Available |
| AI usage policy template | TBD 1-page template | Learner fill-in | ☐ TBD |

---

## 4. Prerequisites

| Item | Minimum Requirement |
|---|---|
| OpenWebUI environment | An existing test environment or a classroom demo environment |
| Accounts | Each student has their own test account; the Admin has a management account |
| Models | At least one usable model; either local Ollama or an API model |
| Test data | Low-sensitivity documents, meeting minutes, emails, FAQs, SOPs |
| Permission draft | Decisions on whether to allow file upload, Web Search, sharing, API Keys, and tool use |
| Company policy | If no AI policy exists, the first version is created in class |

---

## 5. L1 OpenWebUI IPOE

| Category | Definition |
|---|---|
| Input | User accounts, low-sensitivity departmental data, prompts, documents, models, groups, permission settings, AI usage policy |
| Process | Sign-in, starting new chats, model selection, prompt practice, document Q&A, chat organization, Admin account review, role/group/permission settings, data case judgement |
| Output | Personal chat records, Prompt Library, low-sensitivity document summaries, AI usage policy, account/group/permission tables, high-frequency work list, L2 Skill candidates |
| Evidence | Sign-in screenshots, personal chat screenshots, Admin Panel screenshots, Groups/Permissions settings, prompt exercises, data case judgement table, Gate 1 acceptance record |

---

## 6. Full Course Versions

### 6.0 Lecture Map (per [`ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md`](ONLINE_COURSE_DESIGN_METHODOLOGY_EN.md) §5.3)

The hour-block structures of §6.1 / §6.2 / §6.3 are listed in their respective sub-sections below. This §6.0 breaks §6.1 (the most commonly used all-hands course) down to the **3-6 minute / lecture** level — usable directly as a recording script. §6.2 and §6.3 lecture maps are expanded using the same pattern before recording.

#### 6.0.1 §6.1 All-Hands User Course Complete Lecture Map (~50 lectures × avg 3.6 min = 180 min / 3 hr)

> Lecture type codes: **TH** = Talking Head / **S** = Screencast / **SL** = Slides / **VS** = Visual / **PR** = Practice / **QZ** = Quiz / **EN** = Engagement activity / **RC** = Section Recap.

##### Section 0 — Introduction (≤ 10 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 0.1 | Welcome to L1 OpenWebUI | TH | 3 | Instructor intro; why this 3-hour course is worth it |
| 0.2 | What you'll learn | TH+SL | 2 | 4 primary LOs preview (§3.1) + what you'll own after |
| 0.3 | **Engagement: complete one of today's tasks with AI** | EN+S | 5 | Each learner logs in → new chat → pick model → prompt to complete one real work item |

##### Section 1 — L1 Positioning (20 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 1.1 | Why enterprises need a unified AI portal | TH | 4 | 5 risks of personal ChatGPT (security / consistency / governance / cost / knowledge retention) |
| 1.2 | Personal ChatGPT vs enterprise-grade AI | SL | 4 | 5-dimension comparison table |
| 1.3 | OpenWebUI design philosophy | SL | 4 | 3-layer RBAC + Additive Permissions + Open Source |
| 1.4 | L1-L5 maturity map intro | SL+VS | 4 | Two axes (scale + AI autonomy) visualized |
| 1.5 | What you'll own after L1 | TH | 4 | 6 deliverables preview (§9) |

##### Section 2 — Login & Personal Chat Area (30 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 2.1 | First login | S | 3 | Use your own account; no sharing |
| 2.2 | UI overview | S | 4 | Left rail / top bar / settings |
| 2.3 | New chat | S | 3 | New Chat button + naming convention |
| 2.4 | Chat history + folders | S | 4 | Drag-categorize / search / pin |
| 2.5 | Personal Prompt setup | S | 5 | Prompt Library start: first personal prompt |
| 2.6 | Personalization | S | 3 | Name / dark mode / language |
| 2.7 | Section recap | RC | 3 | 5 key points + common mistakes |
| 2.8 | **Formative: mini-demo** | PR | 5 | Learner completes 1 Email Prompt + screenshot on the spot |

##### Section 3 — Model & Chat Operations (30 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 3.1 | Model selector | S | 4 | List / switch / default |
| 3.2 | Regenerate + conversation branching | S | 4 | Regenerate usage |
| 3.3 | Continue & stop | S | 3 | Continue / Stop |
| 3.4 | Export chat | S | 3 | Export JSON / Markdown |
| 3.5 | Organize / rename / tag | S | 4 | Maintain your chat library |
| 3.6 | Archive & delete | S | 3 | Archive vs Delete |
| 3.7 | Section recap | RC | 3 | 6 operation points |
| 3.8 | **Formative: mini-demo** | PR | 6 | Run same question on 3 models, compare |

##### Section 4 — Prompt Foundations (45 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 4.1 | 5 elements of a good prompt | SL+VS | 5 | Role / task / context / constraints / output |
| 4.2 | Example 1: Email draft | S | 4 | From 1 line to full email |
| 4.3 | Example 2: Meeting minute summary | S | 4 | 5-point summary + action items |
| 4.4 | Example 3: Report draft for manager | S | 4 | Weekly / monthly report |
| 4.5 | Example 4: FAQ writing | S | 4 | Customer service / HR usage |
| 4.6 | Example 5: Translation & proofreading | S | 4 | EN/ZH + style choices |
| 4.7 | Iterative refinement | TH | 4 | How to make output better |
| 4.8 | Save to Prompt Library | S | 4 | Your 5 most-used prompts |
| 4.9 | Section recap | RC | 3 | 5 elements + 5 examples + iteration |
| 4.10 | **Formative: write your 5 prompts** | PR | 9 | Learner does + peer review |

##### Section 5 — Daily Work Application (45 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 5.1 | Scenario: Business email in English | S | 6 | Includes client negotiation + empathy framing |
| 5.2 | Scenario: 30-min meeting recording → 5-point summary | S | 6 | Upload transcript → summary |
| 5.3 | Scenario: Complaint response | S | 5 | Empathy + resolution |
| 5.4 | Scenario: Weekly report draft | S | 5 | From scattered notes to structured report |
| 5.5 | Scenario: Presentation outline | S | 5 | Client pitch / internal share |
| 5.6 | Scenario: Long article → LinkedIn post | S | 4 | Content reuse |
| 5.7 | Scenario: Competitor comparison table | S | 5 | Structured data extraction |
| 5.8 | Section recap | RC | 3 | 7 scenarios + per-department application |
| 5.9 | **Formative: pick 3 of your own scenarios** | PR | 6 | Cross-department peer help |

##### Section 6 — Data Boundary (30 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 6.1 | 3 data-leak case studies | SL | 4 | Samsung / Air Canada / Anthropic-style real-event-inspired cases |
| 6.2 | 4 sensitivity tiers | SL+VS | 5 | Public / internal / sensitive / forbidden visualization |
| 6.3 | Decision flow: 3 questions | SL+VS | 4 | Flowchart: can this be input |
| 6.4 | De-identification techniques intro | S | 4 | Replace PII / abstract |
| 6.5 | When human confirmation is needed | SL | 4 | HITL required scenarios |
| 6.6 | Section recap | RC | 3 | 4 tiers + 3 questions + de-id + HITL |
| 6.7 | **Formative: 10-case Quiz** | QZ | 6 | Must score 100% |

##### Section 7 — Conclusion (10 min)

| # | Lecture title | Type | Min | Content / Hook |
| --- | --- | --- | --- | --- |
| 7.1 | Course recap | TH | 3 | Did you build all 6 deliverables? |
| 7.2 | What your Prompt Library should look like | SL | 3 | Examples of mature organization's Prompt Library |
| 7.3 | L2 preview | TH | 3 | Turn prompts into Skills / work into Workflows |
| 7.4 | Congratulations + Bonus | TH | 2 | Closing + next steps |

##### §6.1 Lecture-type mix (per §8.3 recommendation: TH 30% / S 40% / SL 20% / other 10%)

| Type | Count | Time share |
| --- | --- | --- |
| TH (talking head) | 8 | ~ 14% |
| S (screencast) | 26 | ~ 52% |
| SL (slides) | 7 | ~ 13% |
| PR / QZ / EN (interactive) | 7 | ~ 17% |
| RC (recap) | 6 | ~ 11% |

> ⚠️ Screencast share at 52% exceeds the recommended 40% — acceptable here because L1 is operation-dense. But during actual recording, insert 30-60 seconds of TH between consecutive S lectures to avoid 20 minutes of pure screen fatigue.

#### 6.0.2 §6.2 Admin / IT Course Lecture Map (stub — expand before recording)

`(Same pattern: 6 sections × 6-8 lectures = ~45 lectures × avg 4 min = 180 min)`

**Section structure:** Intro (10 min) → Admin Panel overview → Account & login management → Groups & permissions → Model & feature controls → Personal area & sharing policy → Gate 1 acceptance → Conclusion. **Expand per §6.0.1 format.**

#### 6.0.3 §6.3 Enterprise Onboarding Workshop Lecture Map (stub — expand before recording)

`(1 day = ~7 hours pure teaching = ~90-100 lectures; recommend splitting into "AM user training + dept scenarios" and "PM admin setup + L2 bridge governance" two half-day mini-courses)`

**Section structure:** AM1 user training → AM2 dept scenarios → PM1 admin setup → PM2 governance + L2 bridge. **Expand per §6.0.1 format.**

---

### 6.1 L1 All-Hands User Course: 3 Hours

| Time | Topic | Content | Output |
|---|---|---|---|
| 20 min | L1 positioning | Why the enterprise needs a unified AI entry point | L1 consensus |
| 30 min | Sign-in and personal chat area | Sign in with your own account, new chat, history, folders, personal settings | Sign-in and chat screenshots |
| 30 min | Model and chat operations | Choose a model, regenerate answers, continue writing, export, organize chats | Hands-on practice |
| 45 min | Prompt fundamentals | Role, task, context, constraints, output format | Personal prompts |
| 45 min | Daily work practice | Email, summary, meeting minutes, manager's report | Classroom output |
| 30 min | Data boundaries | What may be input, what may not, what requires human confirmation | Data case judgement |

### 6.2 L1 Admin / IT Management Course: 3 Hours

| Time | Topic | Content | Output |
|---|---|---|---|
| 30 min | Admin Panel overview | Users, models, connections, features, documents, announcements | Admin operation checklist |
| 45 min | Account and sign-in management | Admin / User / Pending, review process, default role | Account management sheet |
| 45 min | Groups and permissions | Default Permissions, Groups, departmental permissions, least privilege | Permission matrix |
| 45 min | Model and feature governance | Model visibility, Web Search, File Upload, API Keys, Tools | Model/feature sheet |
| 30 min | Personal area and sharing policy | Personal chats, chat sharing, public resources, export policy | Sharing policy |
| 45 min | Gate 1 acceptance | Environment, accounts, permissions, data policy, Prompt Library | Gate 1 sheet |

### 6.3 L1 Enterprise Adoption Workshop: 1 Day

| Time | Topic | Content | Output |
|---|---|---|---|
| AM 1 | User training | Sign-in, chat, model, prompt, documents | Personal exercises |
| AM 2 | Departmental scenarios | Email, meetings, summaries, reports, FAQ | Departmental prompts |
| PM 1 | Admin configuration | Accounts, roles, groups, permissions, models | Admin Runbook |
| PM 2 | Governance and bridge to L2 | Data policy, high-frequency work, Skill candidates | L2 candidate list |

---

## 7. Admin Control Sheet

| Control | Recommended Setting | Evidence |
|---|---|---|
| First Admin | Assign IT or the AI management focal point | Admin roster |
| User activation | New users default to Pending or controlled User | Default role setting |
| Personal accounts | Prohibit shared accounts; each person signs in individually | Account list |
| Personal chat area | Each user creates their own chats and folders | User screenshots |
| Groups | Build groups by department, seed team, and Power Users | Groups sheet |
| Default Permissions | Adopt least privilege; advanced features opened through Groups | Permission screenshots |
| File Upload | Allow low-sensitivity data first; high-sensitivity data prohibited | Data classification policy |
| Web Search | Open per departmental need; require source citation | Permission sheet |
| API Keys | Disabled by default or granted only to IT / Power Users | API Key policy |
| Share / Public | Public sharing restricted by default | Sharing policy |
| Models | Open local or API models per group | Model access sheet |
| Announcements | Place AI usage policy and data reminders | Banner screenshots |

---

## 8. User Operation Standards

By the end of L1, every student must be able to:

1. Sign in with their own account.
2. Create at least 2 chat topics.
3. Organize chats into folders with clear naming.
4. Build 3 personal go-to prompts.
5. Complete summaries or Q&A on a low-sensitivity document.
6. Judge whether 10 data cases may be input into AI.
7. Know which scenarios require human confirmation rather than accepting the AI answer directly.

---

## 9. Reference Video Map

| # | Video Title | Course Use | Recommendation |
|---:|---|---|---|
| 1 | Open WebUI: The Free, Private ChatGPT Alternative | L1 opener and value positioning | Required |
| 2 | How to Install OpenWebUI | IT installation and environment preparation | Required for IT |
| 3 | Local AI Model Requirements | Hardware requirements for local models | Required for IT |
| 4 | Exploring the OpenWebUI Admin Panel | Admin Panel, enterprise management | Required for Admins |
| 5 | Exploring Open WebUI: Features, Models, & Tools | Feature overview, concepts of models and tools | Required |
| 6 | Chat with Your Own Documents | File upload and document Q&A | Required |
| 7 | Add Real-Time Web Search | Web Search configuration and use | Elective / permission discussion |
| 8 | Add GPT-4 to Open WebUI | OpenAI API provider configuration | IT / Admin |
| 9 | Community Tools | Community tool adoption | Elective, requires security review |
| 10 | Text-to-Speech with ElevenLabs | TTS | Elective |
| 11 | Create Custom AI Models | Model presets, persona models | Admin / pre-L2 |
| 12 | Generate AI Images with DALL-E 3 | Image generation | Elective |
| 13 | LLAVA Multimodal / GPT-4 Image Analysis | Multimodal image analysis | Elective |
| 14 | AI Clone | Personalization demo | Inspiration, not core |
| 15 | Functions to Build Websites & Apps | Functions in action | L2 / L3 preview |
| 16 | Reduce Hallucinations with Advanced Parameters | Hallucinations, parameters, human confirmation | Required |
| 17 | Choosing the Right Ollama Model | Local model selection | IT / Admin |
| 18 | Mobile Access with Ngrok | Mobile access, remote risks | Elective, requires security review |
| 19 | Choosing the Best Language Model | Model selection methodology | Admin / seed users |
| 20 | Vision LLMs for Local Inference | Vision model evaluation | Elective |
| 21 | AI Recruiter Meets AI Clone | Recruiting demo | HR inspiration |
| 22 | Groq Cloud & OpenWebUI | Cloud model provider | IT / Admin |
| 23 | Docker & Watchtower | Operations and updates | Required for IT |
| 24 | OpenWebUI Pipelines | Pipelines | L3 preview |
| 25 | User Roles in Open Web UI | User roles and secure collaboration | Required for Admins |
| 26 | Writing Better Prompts | Prompt fundamentals | Required for everyone |
| 27 | Arena Model | Model testing and comparison | Admin / evaluation |
| 28 | Run Text-to-Speech Locally | Local TTS | Elective |
| 29 | OpenWebUI Memory Explained | Memory / personalized memory | Elective, requires privacy discussion |
| 30 | Quantization | Quantization and performance | Elective for IT |
| 31 | AI-Powered Recruiter Bot | Recruiting Bot | HR / L2 case |
| 32 | Open WebUI v0.4 Updates | Version update concepts | IT / Admin |
| 33 | Anthropic Claude Models | Claude provider configuration | IT / Admin |
| 34 | Public Access to Chatbots | Publicly shared Chatbot | Elective, requires strict enterprise control |
| 35 | Tools, Functions & Pipelines Deep Dive | Advanced extensibility | L2 / L3 preview |
| 36 | Online? Offline? Both? | Cloud / local / Hybrid discussion | Required |

---

## 10. L1 Deliverables

| Deliverable | Description | Acceptance Method |
|---|---|---|
| OpenWebUI user operation manual | Sign-in, chat, model, documents, prompts, data boundaries | Users can follow the manual to operate |
| Admin Runbook | Accounts, roles, groups, permissions, models, feature toggles | Admins can operate independently |
| Account / group / permission table | Personal accounts, departmental groups, feature permissions | IT / HR sign-off |
| AI usage policy | What may be input, what may not, what requires human confirmation | Management confirmation |
| Prompt Library v1 | Personal and departmental go-to prompts | Classroom exercise |
| High-frequency work list | Work suitable for L2 Skill conversion | Department head confirmation |
| Gate 1 acceptance sheet | Environment, accounts, permissions, data policy, exercises | Pass / Fail |

---

## 11. Gate 1: Eligibility to Enter L2

| Gate | Check Question | Required Evidence | Verdict |
|---|---|---|---|
| Gate 1A: Entry point operational | Can OpenWebUI be signed into, models selected, and chats run? | Sign-in and chat screenshots | Pass / Fail |
| Gate 1B: Accounts manageable | Does each student have their own account, and can Admins review and manage them? | Account sheet, Admin Panel screenshots | Pass / Fail |
| Gate 1C: Personal area usable | Can users create their own chats, history, and folders? | User operation screenshots | Pass / Fail |
| Gate 1D: Permissions controllable | Are roles, groups, features, models, and sharing configured? | RBAC / permission sheet | Pass / Fail |
| Gate 1E: Data policy in place | Are rules defined for permitted input, prohibited input, and human confirmation? | AI usage policy | Pass / Fail |
| Gate 1F: L2 candidates clear | Have high-frequency tasks and Skill candidates been organized? | High-frequency work list | Pass / Fail |

If Gates 1B-1D do not pass, large-scale rollout to employees is not recommended. If Gate 1E does not pass, advanced features such as file upload, Web Search, Tools, and Memory should not be introduced.
